artificial intelligence and its impact on society essay

Artificial intelligence is transforming our world — it is on all of us to make sure that it goes well

How ai gets built is currently decided by a small group of technologists. as this technology is transforming our lives, it should be in all of our interest to become informed and engaged..

Why should you care about the development of artificial intelligence?

Think about what the alternative would look like. If you and the wider public do not get informed and engaged, then we leave it to a few entrepreneurs and engineers to decide how this technology will transform our world.

That is the status quo. This small number of people at a few tech firms directly working on artificial intelligence (AI) do understand how extraordinarily powerful this technology is becoming . If the rest of society does not become engaged, then it will be this small elite who decides how this technology will change our lives.

To change this status quo, I want to answer three questions in this article: Why is it hard to take the prospect of a world transformed by AI seriously? How can we imagine such a world? And what is at stake as this technology becomes more powerful?

Why is it hard to take the prospect of a world transformed by artificial intelligence seriously?

In some way, it should be obvious how technology can fundamentally transform the world. We just have to look at how much the world has already changed. If you could invite a family of hunter-gatherers from 20,000 years ago on your next flight, they would be pretty surprised. Technology has changed our world already, so we should expect that it can happen again.

But while we have seen the world transform before, we have seen these transformations play out over the course of generations. What is different now is how very rapid these technological changes have become. In the past, the technologies that our ancestors used in their childhood were still central to their lives in their old age. This has not been the case anymore for recent generations. Instead, it has become common that technologies unimaginable in one's youth become ordinary in later life.

This is the first reason we might not take the prospect seriously: it is easy to underestimate the speed at which technology can change the world.

The second reason why it is difficult to take the possibility of transformative AI – potentially even AI as intelligent as humans – seriously is that it is an idea that we first heard in the cinema. It is not surprising that for many of us, the first reaction to a scenario in which machines have human-like capabilities is the same as if you had asked us to take seriously a future in which vampires, werewolves, or zombies roam the planet. 1

But, it is plausible that it is both the stuff of sci-fi fantasy and the central invention that could arrive in our, or our children’s, lifetimes.

The third reason why it is difficult to take this prospect seriously is by failing to see that powerful AI could lead to very large changes. This is also understandable. It is difficult to form an idea of a future that is very different from our own time. There are two concepts that I find helpful in imagining a very different future with artificial intelligence. Let’s look at both of them.

How to develop an idea of what the future of artificial intelligence might look like?

When thinking about the future of artificial intelligence, I find it helpful to consider two different concepts in particular: human-level AI, and transformative AI. 2 The first concept highlights the AI’s capabilities and anchors them to a familiar benchmark, while transformative AI emphasizes the impact that this technology would have on the world.

From where we are today, much of this may sound like science fiction. It is therefore worth keeping in mind that the majority of surveyed AI experts believe there is a real chance that human-level artificial intelligence will be developed within the next decades, and some believe that it will exist much sooner.

The advantages and disadvantages of comparing machine and human intelligence

One way to think about human-level artificial intelligence is to contrast it with the current state of AI technology. While today’s AI systems often have capabilities similar to a particular, limited part of the human mind, a human-level AI would be a machine that is capable of carrying out the same range of intellectual tasks that we humans are capable of. 3 It is a machine that would be “able to learn to do anything that a human can do,” as Norvig and Russell put it in their textbook on AI. 4

Taken together, the range of abilities that characterize intelligence gives humans the ability to solve problems and achieve a wide variety of goals. A human-level AI would therefore be a system that could solve all those problems that we humans can solve, and do the tasks that humans do today. Such a machine, or collective of machines, would be able to do the work of a translator, an accountant, an illustrator, a teacher, a therapist, a truck driver, or the work of a trader on the world’s financial markets. Like us, it would also be able to do research and science, and to develop new technologies based on that.

The concept of human-level AI has some clear advantages. Using the familiarity of our own intelligence as a reference provides us with some clear guidance on how to imagine the capabilities of this technology.

However, it also has clear disadvantages. Anchoring the imagination of future AI systems to the familiar reality of human intelligence carries the risk that it obscures the very real differences between them.

Some of these differences are obvious. For example, AI systems will have the immense memory of computer systems, against which our own capacity to store information pales. Another obvious difference is the speed at which a machine can absorb and process information. But information storage and processing speed are not the only differences. The domains in which machines already outperform humans is steadily increasing: in chess, after matching the level of the best human players in the late 90s, AI systems reached superhuman levels more than a decade ago. In other games like Go or complex strategy games, this has happened more recently. 5

These differences mean that an AI that is at least as good as humans in every domain would overall be much more powerful than the human mind. Even the first “human-level AI” would therefore be quite superhuman in many ways. 6

Human intelligence is also a bad metaphor for machine intelligence in other ways. The way we think is often very different from machines, and as a consequence the output of thinking machines can be very alien to us.

Most perplexing and most concerning are the strange and unexpected ways in which machine intelligence can fail. The AI-generated image of the horse below provides an example: on the one hand, AIs can do what no human can do – produce an image of anything, in any style (here photorealistic), in mere seconds – but on the other hand it can fail in ways that no human would fail. 7 No human would make the mistake of drawing a horse with five legs. 8

Imagining a powerful future AI as just another human would therefore likely be a mistake. The differences might be so large that it will be a misnomer to call such systems “human-level.”

AI-generated image of a horse 9

Transformative artificial intelligence is defined by the impact this technology would have on the world

In contrast, the concept of transformative AI is not based on a comparison with human intelligence. This has the advantage of sidestepping the problems that the comparisons with our own mind bring. But it has the disadvantage that it is harder to imagine what such a system would look like and be capable of. It requires more from us. It requires us to imagine a world with intelligent actors that are potentially very different from ourselves.

Transformative AI is not defined by any specific capabilities, but by the real-world impact that the AI would have. To qualify as transformative, researchers think of it as AI that is “powerful enough to bring us into a new, qualitatively different future.” 10

In humanity’s history, there have been two cases of such major transformations, the agricultural and the industrial revolutions.

Transformative AI becoming a reality would be an event on that scale. Like the arrival of agriculture 10,000 years ago, or the transition from hand- to machine-manufacturing, it would be an event that would change the world for billions of people around the globe and for the entire trajectory of humanity’s future .

Technologies that fundamentally change how a wide range of goods or services are produced are called ‘general-purpose technologies’. The two previous transformative events were caused by the discovery of two particularly significant general-purpose technologies: the change in food production as humanity transitioned from hunting and gathering to farming, and the rise of machine manufacturing in the industrial revolution. Based on the evidence and arguments presented in this series on AI development, I believe it is plausible that powerful AI could represent the introduction of a similarly significant general-purpose technology.

Timeline of the three transformative events in world history

A future of human-level or transformative AI?

The two concepts are closely related, but they are not the same. The creation of a human-level AI would certainly have a transformative impact on our world. If the work of most humans could be carried out by an AI, the lives of millions of people would change. 11

The opposite, however, is not true: we might see transformative AI without developing human-level AI. Since the human mind is in many ways a poor metaphor for the intelligence of machines, we might plausibly develop transformative AI before we develop human-level AI. Depending on how this goes, this might mean that we will never see any machine intelligence for which human intelligence is a helpful comparison.

When and if AI systems might reach either of these levels is of course difficult to predict. In my companion article on this question, I give an overview of what researchers in this field currently believe. Many AI experts believe there is a real chance that such systems will be developed within the next decades, and some believe that they will exist much sooner.

What is at stake as artificial intelligence becomes more powerful?

All major technological innovations lead to a range of positive and negative consequences. For AI, the spectrum of possible outcomes – from the most negative to the most positive – is extraordinarily wide.

That the use of AI technology can cause harm is clear, because it is already happening.

AI systems can cause harm when people use them maliciously. For example, when they are used in politically-motivated disinformation campaigns or to enable mass surveillance. 12

But AI systems can also cause unintended harm, when they act differently than intended or fail. For example, in the Netherlands the authorities used an AI system which falsely claimed that an estimated 26,000 parents made fraudulent claims for child care benefits. The false allegations led to hardship for many poor families, and also resulted in the resignation of the Dutch government in 2021. 13

As AI becomes more powerful, the possible negative impacts could become much larger. Many of these risks have rightfully received public attention: more powerful AI could lead to mass labor displacement, or extreme concentrations of power and wealth. In the hands of autocrats, it could empower totalitarianism through its suitability for mass surveillance and control.

The so-called alignment problem of AI is another extreme risk. This is the concern that nobody would be able to control a powerful AI system, even if the AI takes actions that harm us humans, or humanity as a whole. This risk is unfortunately receiving little attention from the wider public, but it is seen as an extremely large risk by many leading AI researchers. 14

How could an AI possibly escape human control and end up harming humans?

The risk is not that an AI becomes self-aware, develops bad intentions, and “chooses” to do this. The risk is that we try to instruct the AI to pursue some specific goal – even a very worthwhile one – and in the pursuit of that goal it ends up harming humans. It is about unintended consequences. The AI does what we told it to do, but not what we wanted it to do.

Can’t we just tell the AI to not do those things? It is definitely possible to build an AI that avoids any particular problem we foresee, but it is hard to foresee all the possible harmful unintended consequences. The alignment problem arises because of “the impossibility of defining true human purposes correctly and completely,” as AI researcher Stuart Russell puts it. 15

Can’t we then just switch off the AI? This might also not be possible. That is because a powerful AI would know two things: it faces a risk that humans could turn it off, and it can’t achieve its goals once it has been turned off. As a consequence, the AI will pursue a very fundamental goal of ensuring that it won’t be switched off. This is why, once we realize that an extremely intelligent AI is causing unintended harm in the pursuit of some specific goal, it might not be possible to turn it off or change what the system does. 16

This risk – that humanity might not be able to stay in control once AI becomes very powerful, and that this might lead to an extreme catastrophe – has been recognized right from the early days of AI research more than 70 years ago. 17 The very rapid development of AI in recent years has made a solution to this problem much more urgent.

I have tried to summarize some of the risks of AI, but a short article is not enough space to address all possible questions. Especially on the very worst risks of AI systems, and what we can do now to reduce them, I recommend reading the book The Alignment Problem by Brian Christian and Benjamin Hilton’s article ‘Preventing an AI-related catastrophe’ .

If we manage to avoid these risks, transformative AI could also lead to very positive consequences. Advances in science and technology were crucial to the many positive developments in humanity’s history. If artificial ingenuity can augment our own, it could help us make progress on the many large problems we face: from cleaner energy, to the replacement of unpleasant work, to much better healthcare.

This extremely large contrast between the possible positives and negatives makes clear that the stakes are unusually high with this technology. Reducing the negative risks and solving the alignment problem could mean the difference between a healthy, flourishing, and wealthy future for humanity – and the destruction of the same.

How can we make sure that the development of AI goes well?

Making sure that the development of artificial intelligence goes well is not just one of the most crucial questions of our time, but likely one of the most crucial questions in human history. This needs public resources – public funding, public attention, and public engagement.

Currently, almost all resources that are dedicated to AI aim to speed up the development of this technology. Efforts that aim to increase the safety of AI systems, on the other hand, do not receive the resources they need. Researcher Toby Ord estimated that in 2020 between $10 to $50 million was spent on work to address the alignment problem. 18 Corporate AI investment in the same year was more than 2000-times larger, it summed up to $153 billion.

This is not only the case for the AI alignment problem. The work on the entire range of negative social consequences from AI is under-resourced compared to the large investments to increase the power and use of AI systems.

It is frustrating and concerning for society as a whole that AI safety work is extremely neglected and that little public funding is dedicated to this crucial field of research. On the other hand, for each individual person this neglect means that they have a good chance to actually make a positive difference, if they dedicate themselves to this problem now. And while the field of AI safety is small, it does provide good resources on what you can do concretely if you want to work on this problem.

I hope that more people dedicate their individual careers to this cause, but it needs more than individual efforts. A technology that is transforming our society needs to be a central interest of all of us. As a society we have to think more about the societal impact of AI, become knowledgeable about the technology, and understand what is at stake.

When our children look back at today, I imagine that they will find it difficult to understand how little attention and resources we dedicated to the development of safe AI. I hope that this changes in the coming years, and that we begin to dedicate more resources to making sure that powerful AI gets developed in a way that benefits us and the next generations.

If we fail to develop this broad-based understanding, then it will remain the small elite that finances and builds this technology that will determine how one of the – or plausibly the – most powerful technology in human history will transform our world.

If we leave the development of artificial intelligence entirely to private companies, then we are also leaving it up these private companies what our future — the future of humanity — will be.

With our work at Our World in Data we want to do our small part to enable a better informed public conversation on AI and the future we want to live in. You can find these resources on OurWorldinData.org/artificial-intelligence

Acknowledgements: I would like to thank my colleagues Daniel Bachler, Charlie Giattino, and Edouard Mathieu for their helpful comments to drafts of this essay.

This problem becomes even larger when we try to imagine how a future with a human-level AI might play out. Any particular scenario will not only involve the idea that this powerful AI exists, but a whole range of additional assumptions about the future context in which this happens. It is therefore hard to communicate a scenario of a world with human-level AI that does not sound contrived, bizarre or even silly.

Both of these concepts are widely used in the scientific literature on artificial intelligence. For example, questions about the timelines for the development of future AI are often framed using these terms. See my article on this topic .

The fact that humans are capable of a range of intellectual tasks means that you arrive at different definitions of intelligence depending on which aspect within that range you focus on (the Wikipedia entry on intelligence , for example, lists a number of definitions from various researchers and different disciplines). As a consequence there are also various definitions of ‘human-level AI’.

There are also several closely related terms: Artificial General Intelligence, High-Level Machine Intelligence, Strong AI, or Full AI are sometimes synonymously used, and sometimes defined in similar, yet different ways. In specific discussions, it is necessary to define this concept more narrowly; for example, in studies on AI timelines researchers offer more precise definitions of what human-level AI refers to in their particular study.

Peter Norvig and Stuart Russell (2021) — Artificial Intelligence: A Modern Approach. Fourth edition. Published by Pearson.

The AI system AlphaGo , and its various successors, won against Go masters. The AI system Pluribus beat humans at no-limit Texas hold 'em poker. The AI system Cicero can strategize and use human language to win the strategy game Diplomacy. See: Meta Fundamental AI Research Diplomacy Team (FAIR), Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, et al. (2022) – ‘Human-Level Play in the Game of Diplomacy by Combining Language Models with Strategic Reasoning’. In Science 0, no. 0 (22 November 2022): eade9097. https://doi.org/10.1126/science.ade9097 .

This also poses a problem when we evaluate how the intelligence of a machine compares with the intelligence of humans. If intelligence was a general ability, a single capacity, then we could easily compare and evaluate it, but the fact that it is a range of skills makes it much more difficult to compare across machine and human intelligence. Tests for AI systems are therefore comprising a wide range of tasks. See for example Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt (2020) –  Measuring Massive Multitask Language Understanding or the definition of what would qualify as artificial general intelligence in this Metaculus prediction .

An overview of how AI systems can fail can be found in Charles Choi – 7 Revealing Ways AIs Fail . It is also worth reading through the AIAAIC Repository which “details recent incidents and controversies driven by or relating to AI, algorithms, and automation."

I have taken this example from AI researcher François Chollet , who published it here .

Via François Chollet , who published it here . Based on Chollet’s comments it seems that this image was created by the AI system ‘Stable Diffusion’.

This quote is from Holden Karnofsky (2021) – AI Timelines: Where the Arguments, and the "Experts," Stand . For Holden Karnofsky’s earlier thinking on this conceptualization of AI see his 2016 article ‘Some Background on Our Views Regarding Advanced Artificial Intelligence’ .

Ajeya Cotra, whose research on AI timelines I discuss in other articles of this series, attempts to give a quantitative definition of what would qualify as transformative AI. in her widely cited report on AI timelines she defines it as a change in software technology that brings the growth rate of gross world product "to 20%-30% per year". Several other researchers define TAI in similar terms.

Human-level AI is typically defined as a software system that can carry out at least 90% or 99% of all economically relevant tasks that humans carry out. A lower-bar definition would be an AI system that can carry out all those tasks that can currently be done by another human who is working remotely on a computer.

On the use of AI in politically-motivated disinformation campaigns see for example John Villasenor (November 2020) – How to deal with AI-enabled disinformation . More generally on this topic see Brundage and Avin et al. (2018) – The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, published at maliciousaireport.com . A starting point for literature and reporting on mass surveillance by governments is the relevant Wikipedia entry .

See for example the Wikipedia entry on the ‘Dutch childcare benefits scandal’ and Melissa Heikkilä (2022) – ‘Dutch scandal serves as a warning for Europe over risks of using algorithms’ , in Politico. The technology can also reinforce discrimination in terms of race and gender. See Brian Christian’s book The Alignment Problem and the reports of the AI Now Institute .

Overviews are provided in Stuart Russell (2019) – Human Compatible (especially chapter 5) and Brian Christian’s 2020 book The Alignment Problem . Christian presents the thinking of many leading AI researchers from the earliest days up to now and presents an excellent overview of this problem. It is also seen as a large risk by some of the leading private firms who work towards powerful AI – see OpenAI's article " Our approach to alignment research " from August 2022.

Stuart Russell (2019) – Human Compatible

A question that follows from this is, why build such a powerful AI in the first place?

The incentives are very high. As I emphasize below, this innovation has the potential to lead to very positive developments. In addition to the large social benefits there are also large incentives for those who develop it – the governments that can use it for their goals, the individuals who can use it to become more powerful and wealthy. Additionally, it is of scientific interest and might help us to understand our own mind and intelligence better. And lastly, even if we wanted to stop building powerful AIs, it is likely very hard to actually achieve it. It is very hard to coordinate across the whole world and agree to stop building more advanced AI – countries around the world would have to agree and then find ways to actually implement it.

In 1950 the computer science pioneer Alan Turing put it like this: “If a machine can think, it might think more intelligently than we do, and then where should we be? … [T]his new danger is much closer. If it comes at all it will almost certainly be within the next millennium. It is remote but not astronomically remote, and is certainly something which can give us anxiety. It is customary, in a talk or article on this subject, to offer a grain of comfort, in the form of a statement that some particularly human characteristic could never be imitated by a machine. … I cannot offer any such comfort, for I believe that no such bounds can be set.” Alan. M. Turing (1950) – Computing Machinery and Intelligence , In Mind, Volume LIX, Issue 236, October 1950, Pages 433–460.

Norbert Wiener is another pioneer who saw the alignment problem very early. One way he put it was “If we use, to achieve our purposes, a mechanical agency with whose operation we cannot interfere effectively … we had better be quite sure that the purpose put into the machine is the purpose which we really desire.” quoted from Norbert Wiener (1960) – Some Moral and Technical Consequences of Automation: As machines learn they may develop unforeseen strategies at rates that baffle their programmers. In Science.

In 1950 – the same year in which Turing published the cited article – Wiener published his book The Human Use of Human Beings, whose front-cover blurb reads: “The ‘mechanical brain’ and similar machines can destroy human values or enable us to realize them as never before.”

Toby Ord – The Precipice . He makes this projection in footnote 55 of chapter 2. It is based on the 2017 estimate by Farquhar.

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The future of AI’s impact on society

As artificial intelligence continues its rapid evolution, what influence do humans have?

• Joanna J. Bryson

Provided by BBVA

The past decade, and particularly the past few years, has been transformative for artificial intelligence, not so much in terms of what we can do with this technology as what we are doing with it. Some place the advent of this era to 2007, with the introduction of smartphones. At its most essential, intelligence is just intelligence, whether artifact or animal. It is a form of computation, and as such, a transformation of information. The cornucopia of deeply personal information that resulted from the willful tethering of a huge portion of society to the internet has allowed us to pass immense explicit and implicit knowledge from human culture via human brains into digital form. Here we can not only use it to operate with human-like competence but also produce further knowledge and behavior by means of machine-based computation.

Joanna J. Bryson is an associate professor of computer science at the University of Bath.

For decades—even prior to the inception of the term—AI has aroused both fear and excitement as humanity contemplates creating machines in our image. This expectation that intelligent artifacts should by necessity be human-like artifacts blinded most of us to the important fact that we have been achieving AI for some time. While the breakthroughs in surpassing human ability at human pursuits, such as chess, make headlines, AI has been a standard part of the industrial repertoire since at least the 1980s. Then production-rule or “expert” systems became a standard technology for checking circuit boards and detecting credit card fraud. Similarly, machine-learning (ML) strategies like genetic algorithms have long been used for intractable computational problems, such as scheduling, and neural networks not only to model and understand human learning, but also for basic industrial control and monitoring.

In the 1990s, probabilistic and Bayesian methods revolutionized ML and opened the door to some of the most pervasive AI technologies now available: searching through massive troves of data. This search capacity included the ability to do semantic analysis of raw text, astonishingly enabling web users to find the documents they seek out of trillions of webpages just by typing only a few words.

AI is core to some of the most successful companies in history in terms of market capitalization—Apple, Alphabet, Microsoft, and Amazon. Along with information and communication technology (ICT) more generally, AI has revolutionized the ease with which people from all over the world can access knowledge, credit, and other benefits of contemporary global society. Such access has helped lead to massive reduction of global inequality and extreme poverty, for example by allowing farmers to know fair prices, the best crops, and giving them access to accurate weather predictions.

For decades, AI has aroused both fear and excitement as humanity contemplates creating machines in our image.

Having said this, academics, technologists, and the general public have raised a number of concerns that may indicate a need for down-regulation or constraint. As Brad Smith, the president of Microsoft recently asserted, “Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses.”

Artificial intelligence is already changing society at a faster pace than we realize, but at the same time it is not as novel or unique in human experience as we are often led to imagine. Other artifactual entities, such as language and writing, corporations and governments, telecommunications and oil, have previously extended our capacities, altered our economies, and disrupted our social order—generally though not universally for the better. The evidence assumption that we are on average better off for our progress is ironically perhaps the greatest hurdle we currently need to overcome: sustainable living and reversing the collapse of biodiversity.

AI and ICT more generally may well require radical innovations in the way we govern, and particularly in the way we raise revenue for redistribution. We are faced with transnational wealth transfers through business innovations that have outstripped our capacity to measure or even identify the level of income generated. Further, this new currency of unknowable value is often personal data, and personal data gives those who hold it the immense power of prediction over the individuals it references.

But beyond the economic and governance challenges, we need to remember that AI first and foremost extends and enhances what it means to be human, and in particular our problem-solving capacities. Given ongoing global challenges such as security, sustainability, and reversing the collapse of biodiversity, such enhancements promise to continue to be of significant benefit, assuming we can establish good mechanisms for their regulation. Through a sensible portfolio of regulatory policies and agencies, we should continue to expand—and also to limit, as appropriate—the scope of potential AI applications.

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How artificial intelligence is transforming the world

Subscribe to the center for technology innovation newsletter, darrell m. west and darrell m. west senior fellow - center for technology innovation , douglas dillon chair in governmental studies john r. allen john r. allen.

April 24, 2018

Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making—and already it is transforming every walk of life. In this report, Darrell West and John Allen discuss AI’s application across a variety of sectors, address issues in its development, and offer recommendations for getting the most out of AI while still protecting important human values.

Table of Contents I. Qualities of artificial intelligence II. Applications in diverse sectors III. Policy, regulatory, and ethical issues IV. Recommendations V. Conclusion

• 49 min read

Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it. 1 A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.

Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance.

In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values. 2

In order to maximize AI benefits, we recommend nine steps for going forward:

• Encourage greater data access for researchers without compromising users’ personal privacy,
• invest more government funding in unclassified AI research,
• promote new models of digital education and AI workforce development so employees have the skills needed in the 21 st -century economy,
• create a federal AI advisory committee to make policy recommendations,
• engage with state and local officials so they enact effective policies,
• regulate broad AI principles rather than specific algorithms,
• take bias complaints seriously so AI does not replicate historic injustice, unfairness, or discrimination in data or algorithms,
• maintain mechanisms for human oversight and control, and
• penalize malicious AI behavior and promote cybersecurity.

Qualities of artificial intelligence

Although there is no uniformly agreed upon definition, AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention.” 3  According to researchers Shubhendu and Vijay, these software systems “make decisions which normally require [a] human level of expertise” and help people anticipate problems or deal with issues as they come up. 4 As such, they operate in an intentional, intelligent, and adaptive manner.

Intentionality

Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.

Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.

Intelligence

AI generally is undertaken in conjunction with machine learning and data analytics. 5 Machine learning takes data and looks for underlying trends. If it spots something that is relevant for a practical problem, software designers can take that knowledge and use it to analyze specific issues. All that is required are data that are sufficiently robust that algorithms can discern useful patterns. Data can come in the form of digital information, satellite imagery, visual information, text, or unstructured data.

Adaptability

AI systems have the ability to learn and adapt as they make decisions. In the transportation area, for example, semi-autonomous vehicles have tools that let drivers and vehicles know about upcoming congestion, potholes, highway construction, or other possible traffic impediments. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their achieved “experience” is immediately and fully transferable to other similarly configured vehicles. Their advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions. And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions.

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Makada Henry-Nickie

November 16, 2017

Sunil Johal, Daniel Araya

February 28, 2017

Applications in diverse sectors

AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities. There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways. 6

One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by PriceWaterhouseCoopers estimated that “artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030.” 7 That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Meanwhile, a McKinsey Global Institute study of China found that “AI-led automation can give the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth annually, depending on the speed of adoption.” 8 Although its authors found that China currently lags the United States and the United Kingdom in AI deployment, the sheer size of its AI market gives that country tremendous opportunities for pilot testing and future development.

Investments in financial AI in the United States tripled between 2013 and 2014 to a total of $12.2 billion. 9 According to observers in that sector, “Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check.” 10 In addition, there are so-called robo-advisers that “create personalized investment portfolios, obviating the need for stockbrokers and financial advisers.” 11 These advances are designed to take the emotion out of investing and undertake decisions based on analytical considerations, and make these choices in a matter of minutes.

A prominent example of this is taking place in stock exchanges, where high-frequency trading by machines has replaced much of human decisionmaking. People submit buy and sell orders, and computers match them in the blink of an eye without human intervention. Machines can spot trading inefficiencies or market differentials on a very small scale and execute trades that make money according to investor instructions. 12 Powered in some places by advanced computing, these tools have much greater capacities for storing information because of their emphasis not on a zero or a one, but on “quantum bits” that can store multiple values in each location. 13 That dramatically increases storage capacity and decreases processing times.

Fraud detection represents another way AI is helpful in financial systems. It sometimes is difficult to discern fraudulent activities in large organizations, but AI can identify abnormalities, outliers, or deviant cases requiring additional investigation. That helps managers find problems early in the cycle, before they reach dangerous levels. 14

National security

AI plays a substantial role in national defense. Through its Project Maven, the American military is deploying AI “to sift through the massive troves of data and video captured by surveillance and then alert human analysts of patterns or when there is abnormal or suspicious activity.” 15 According to Deputy Secretary of Defense Patrick Shanahan, the goal of emerging technologies in this area is “to meet our warfighters’ needs and to increase [the] speed and agility [of] technology development and procurement.” 16

Artificial intelligence will accelerate the traditional process of warfare so rapidly that a new term has been coined: hyperwar.

The big data analytics associated with AI will profoundly affect intelligence analysis, as massive amounts of data are sifted in near real time—if not eventually in real time—thereby providing commanders and their staffs a level of intelligence analysis and productivity heretofore unseen. Command and control will similarly be affected as human commanders delegate certain routine, and in special circumstances, key decisions to AI platforms, reducing dramatically the time associated with the decision and subsequent action. In the end, warfare is a time competitive process, where the side able to decide the fastest and move most quickly to execution will generally prevail. Indeed, artificially intelligent intelligence systems, tied to AI-assisted command and control systems, can move decision support and decisionmaking to a speed vastly superior to the speeds of the traditional means of waging war. So fast will be this process, especially if coupled to automatic decisions to launch artificially intelligent autonomous weapons systems capable of lethal outcomes, that a new term has been coined specifically to embrace the speed at which war will be waged: hyperwar.

While the ethical and legal debate is raging over whether America will ever wage war with artificially intelligent autonomous lethal systems, the Chinese and Russians are not nearly so mired in this debate, and we should anticipate our need to defend against these systems operating at hyperwar speeds. The challenge in the West of where to position “humans in the loop” in a hyperwar scenario will ultimately dictate the West’s capacity to be competitive in this new form of conflict. 17

Just as AI will profoundly affect the speed of warfare, the proliferation of zero day or zero second cyber threats as well as polymorphic malware will challenge even the most sophisticated signature-based cyber protection. This forces significant improvement to existing cyber defenses. Increasingly, vulnerable systems are migrating, and will need to shift to a layered approach to cybersecurity with cloud-based, cognitive AI platforms. This approach moves the community toward a “thinking” defensive capability that can defend networks through constant training on known threats. This capability includes DNA-level analysis of heretofore unknown code, with the possibility of recognizing and stopping inbound malicious code by recognizing a string component of the file. This is how certain key U.S.-based systems stopped the debilitating “WannaCry” and “Petya” viruses.

Preparing for hyperwar and defending critical cyber networks must become a high priority because China, Russia, North Korea, and other countries are putting substantial resources into AI. In 2017, China’s State Council issued a plan for the country to “build a domestic industry worth almost $150 billion” by 2030. 18 As an example of the possibilities, the Chinese search firm Baidu has pioneered a facial recognition application that finds missing people. In addition, cities such as Shenzhen are providing up to $1 million to support AI labs. That country hopes AI will provide security, combat terrorism, and improve speech recognition programs. 19 The dual-use nature of many AI algorithms will mean AI research focused on one sector of society can be rapidly modified for use in the security sector as well. 20

Health care

AI tools are helping designers improve computational sophistication in health care. For example, Merantix is a German company that applies deep learning to medical issues. It has an application in medical imaging that “detects lymph nodes in the human body in Computer Tomography (CT) images.” 21 According to its developers, the key is labeling the nodes and identifying small lesions or growths that could be problematic. Humans can do this, but radiologists charge $100 per hour and may be able to carefully read only four images an hour. If there were 10,000 images, the cost of this process would be $250,000, which is prohibitively expensive if done by humans.

What deep learning can do in this situation is train computers on data sets to learn what a normal-looking versus an irregular-appearing lymph node is. After doing that through imaging exercises and honing the accuracy of the labeling, radiological imaging specialists can apply this knowledge to actual patients and determine the extent to which someone is at risk of cancerous lymph nodes. Since only a few are likely to test positive, it is a matter of identifying the unhealthy versus healthy node.

AI has been applied to congestive heart failure as well, an illness that afflicts 10 percent of senior citizens and costs $35 billion each year in the United States. AI tools are helpful because they “predict in advance potential challenges ahead and allocate resources to patient education, sensing, and proactive interventions that keep patients out of the hospital.” 22

Criminal justice

AI is being deployed in the criminal justice area. The city of Chicago has developed an AI-driven “Strategic Subject List” that analyzes people who have been arrested for their risk of becoming future perpetrators. It ranks 400,000 people on a scale of 0 to 500, using items such as age, criminal activity, victimization, drug arrest records, and gang affiliation. In looking at the data, analysts found that youth is a strong predictor of violence, being a shooting victim is associated with becoming a future perpetrator, gang affiliation has little predictive value, and drug arrests are not significantly associated with future criminal activity. 23

Judicial experts claim AI programs reduce human bias in law enforcement and leads to a fairer sentencing system. R Street Institute Associate Caleb Watney writes:

Empirically grounded questions of predictive risk analysis play to the strengths of machine learning, automated reasoning and other forms of AI. One machine-learning policy simulation concluded that such programs could be used to cut crime up to 24.8 percent with no change in jailing rates, or reduce jail populations by up to 42 percent with no increase in crime rates. 24

However, critics worry that AI algorithms represent “a secret system to punish citizens for crimes they haven’t yet committed. The risk scores have been used numerous times to guide large-scale roundups.” 25 The fear is that such tools target people of color unfairly and have not helped Chicago reduce the murder wave that has plagued it in recent years.

Despite these concerns, other countries are moving ahead with rapid deployment in this area. In China, for example, companies already have “considerable resources and access to voices, faces and other biometric data in vast quantities, which would help them develop their technologies.” 26 New technologies make it possible to match images and voices with other types of information, and to use AI on these combined data sets to improve law enforcement and national security. Through its “Sharp Eyes” program, Chinese law enforcement is matching video images, social media activity, online purchases, travel records, and personal identity into a “police cloud.” This integrated database enables authorities to keep track of criminals, potential law-breakers, and terrorists. 27 Put differently, China has become the world’s leading AI-powered surveillance state.

Transportation

Transportation represents an area where AI and machine learning are producing major innovations. Research by Cameron Kerry and Jack Karsten of the Brookings Institution has found that over $80 billion was invested in autonomous vehicle technology between August 2014 and June 2017. Those investments include applications both for autonomous driving and the core technologies vital to that sector. 28

Autonomous vehicles—cars, trucks, buses, and drone delivery systems—use advanced technological capabilities. Those features include automated vehicle guidance and braking, lane-changing systems, the use of cameras and sensors for collision avoidance, the use of AI to analyze information in real time, and the use of high-performance computing and deep learning systems to adapt to new circumstances through detailed maps. 29

Light detection and ranging systems (LIDARs) and AI are key to navigation and collision avoidance. LIDAR systems combine light and radar instruments. They are mounted on the top of vehicles that use imaging in a 360-degree environment from a radar and light beams to measure the speed and distance of surrounding objects. Along with sensors placed on the front, sides, and back of the vehicle, these instruments provide information that keeps fast-moving cars and trucks in their own lane, helps them avoid other vehicles, applies brakes and steering when needed, and does so instantly so as to avoid accidents.

Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. This means that software is the key—not the physical car or truck itself.

Since these cameras and sensors compile a huge amount of information and need to process it instantly to avoid the car in the next lane, autonomous vehicles require high-performance computing, advanced algorithms, and deep learning systems to adapt to new scenarios. This means that software is the key, not the physical car or truck itself. 30 Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. 31

Ride-sharing companies are very interested in autonomous vehicles. They see advantages in terms of customer service and labor productivity. All of the major ride-sharing companies are exploring driverless cars. The surge of car-sharing and taxi services—such as Uber and Lyft in the United States, Daimler’s Mytaxi and Hailo service in Great Britain, and Didi Chuxing in China—demonstrate the opportunities of this transportation option. Uber recently signed an agreement to purchase 24,000 autonomous cars from Volvo for its ride-sharing service. 32

However, the ride-sharing firm suffered a setback in March 2018 when one of its autonomous vehicles in Arizona hit and killed a pedestrian. Uber and several auto manufacturers immediately suspended testing and launched investigations into what went wrong and how the fatality could have occurred. 33 Both industry and consumers want reassurance that the technology is safe and able to deliver on its stated promises. Unless there are persuasive answers, this accident could slow AI advancements in the transportation sector.

Smart cities

Metropolitan governments are using AI to improve urban service delivery. For example, according to Kevin Desouza, Rashmi Krishnamurthy, and Gregory Dawson:

The Cincinnati Fire Department is using data analytics to optimize medical emergency responses. The new analytics system recommends to the dispatcher an appropriate response to a medical emergency call—whether a patient can be treated on-site or needs to be taken to the hospital—by taking into account several factors, such as the type of call, location, weather, and similar calls. 34

Since it fields 80,000 requests each year, Cincinnati officials are deploying this technology to prioritize responses and determine the best ways to handle emergencies. They see AI as a way to deal with large volumes of data and figure out efficient ways of responding to public requests. Rather than address service issues in an ad hoc manner, authorities are trying to be proactive in how they provide urban services.

Cincinnati is not alone. A number of metropolitan areas are adopting smart city applications that use AI to improve service delivery, environmental planning, resource management, energy utilization, and crime prevention, among other things. For its smart cities index, the magazine Fast Company ranked American locales and found Seattle, Boston, San Francisco, Washington, D.C., and New York City as the top adopters. Seattle, for example, has embraced sustainability and is using AI to manage energy usage and resource management. Boston has launched a “City Hall To Go” that makes sure underserved communities receive needed public services. It also has deployed “cameras and inductive loops to manage traffic and acoustic sensors to identify gun shots.” San Francisco has certified 203 buildings as meeting LEED sustainability standards. 35

Through these and other means, metropolitan areas are leading the country in the deployment of AI solutions. Indeed, according to a National League of Cities report, 66 percent of American cities are investing in smart city technology. Among the top applications noted in the report are “smart meters for utilities, intelligent traffic signals, e-governance applications, Wi-Fi kiosks, and radio frequency identification sensors in pavement.” 36

Policy, regulatory, and ethical issues

These examples from a variety of sectors demonstrate how AI is transforming many walks of human existence. The increasing penetration of AI and autonomous devices into many aspects of life is altering basic operations and decisionmaking within organizations, and improving efficiency and response times.

At the same time, though, these developments raise important policy, regulatory, and ethical issues. For example, how should we promote data access? How do we guard against biased or unfair data used in algorithms? What types of ethical principles are introduced through software programming, and how transparent should designers be about their choices? What about questions of legal liability in cases where algorithms cause harm? 37

The increasing penetration of AI into many aspects of life is altering decisionmaking within organizations and improving efficiency. At the same time, though, these developments raise important policy, regulatory, and ethical issues.

Data access problems

The key to getting the most out of AI is having a “data-friendly ecosystem with unified standards and cross-platform sharing.” AI depends on data that can be analyzed in real time and brought to bear on concrete problems. Having data that are “accessible for exploration” in the research community is a prerequisite for successful AI development. 38

According to a McKinsey Global Institute study, nations that promote open data sources and data sharing are the ones most likely to see AI advances. In this regard, the United States has a substantial advantage over China. Global ratings on data openness show that U.S. ranks eighth overall in the world, compared to 93 for China. 39

But right now, the United States does not have a coherent national data strategy. There are few protocols for promoting research access or platforms that make it possible to gain new insights from proprietary data. It is not always clear who owns data or how much belongs in the public sphere. These uncertainties limit the innovation economy and act as a drag on academic research. In the following section, we outline ways to improve data access for researchers.

Biases in data and algorithms

In some instances, certain AI systems are thought to have enabled discriminatory or biased practices. 40 For example, Airbnb has been accused of having homeowners on its platform who discriminate against racial minorities. A research project undertaken by the Harvard Business School found that “Airbnb users with distinctly African American names were roughly 16 percent less likely to be accepted as guests than those with distinctly white names.” 41

Racial issues also come up with facial recognition software. Most such systems operate by comparing a person’s face to a range of faces in a large database. As pointed out by Joy Buolamwini of the Algorithmic Justice League, “If your facial recognition data contains mostly Caucasian faces, that’s what your program will learn to recognize.” 42 Unless the databases have access to diverse data, these programs perform poorly when attempting to recognize African-American or Asian-American features.

Many historical data sets reflect traditional values, which may or may not represent the preferences wanted in a current system. As Buolamwini notes, such an approach risks repeating inequities of the past:

The rise of automation and the increased reliance on algorithms for high-stakes decisions such as whether someone get insurance or not, your likelihood to default on a loan or somebody’s risk of recidivism means this is something that needs to be addressed. Even admissions decisions are increasingly automated—what school our children go to and what opportunities they have. We don’t have to bring the structural inequalities of the past into the future we create. 43

AI ethics and transparency

Algorithms embed ethical considerations and value choices into program decisions. As such, these systems raise questions concerning the criteria used in automated decisionmaking. Some people want to have a better understanding of how algorithms function and what choices are being made. 44

In the United States, many urban schools use algorithms for enrollment decisions based on a variety of considerations, such as parent preferences, neighborhood qualities, income level, and demographic background. According to Brookings researcher Jon Valant, the New Orleans–based Bricolage Academy “gives priority to economically disadvantaged applicants for up to 33 percent of available seats. In practice, though, most cities have opted for categories that prioritize siblings of current students, children of school employees, and families that live in school’s broad geographic area.” 45 Enrollment choices can be expected to be very different when considerations of this sort come into play.

Depending on how AI systems are set up, they can facilitate the redlining of mortgage applications, help people discriminate against individuals they don’t like, or help screen or build rosters of individuals based on unfair criteria. The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers. 46

For these reasons, the EU is implementing the General Data Protection Regulation (GDPR) in May 2018. The rules specify that people have “the right to opt out of personally tailored ads” and “can contest ‘legal or similarly significant’ decisions made by algorithms and appeal for human intervention” in the form of an explanation of how the algorithm generated a particular outcome. Each guideline is designed to ensure the protection of personal data and provide individuals with information on how the “black box” operates. 47

Legal liability

There are questions concerning the legal liability of AI systems. If there are harms or infractions (or fatalities in the case of driverless cars), the operators of the algorithm likely will fall under product liability rules. A body of case law has shown that the situation’s facts and circumstances determine liability and influence the kind of penalties that are imposed. Those can range from civil fines to imprisonment for major harms. 48 The Uber-related fatality in Arizona will be an important test case for legal liability. The state actively recruited Uber to test its autonomous vehicles and gave the company considerable latitude in terms of road testing. It remains to be seen if there will be lawsuits in this case and who is sued: the human backup driver, the state of Arizona, the Phoenix suburb where the accident took place, Uber, software developers, or the auto manufacturer. Given the multiple people and organizations involved in the road testing, there are many legal questions to be resolved.

In non-transportation areas, digital platforms often have limited liability for what happens on their sites. For example, in the case of Airbnb, the firm “requires that people agree to waive their right to sue, or to join in any class-action lawsuit or class-action arbitration, to use the service.” By demanding that its users sacrifice basic rights, the company limits consumer protections and therefore curtails the ability of people to fight discrimination arising from unfair algorithms. 49 But whether the principle of neutral networks holds up in many sectors is yet to be determined on a widespread basis.

Recommendations

In order to balance innovation with basic human values, we propose a number of recommendations for moving forward with AI. This includes improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.

Improving data access

The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design. AI requires data to test and improve its learning capacity. 50 Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.

In general, the research community needs better access to government and business data, although with appropriate safeguards to make sure researchers do not misuse data in the way Cambridge Analytica did with Facebook information. There is a variety of ways researchers could gain data access. One is through voluntary agreements with companies holding proprietary data. Facebook, for example, recently announced a partnership with Stanford economist Raj Chetty to use its social media data to explore inequality. 51 As part of the arrangement, researchers were required to undergo background checks and could only access data from secured sites in order to protect user privacy and security.

In the U.S., there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design.

Google long has made available search results in aggregated form for researchers and the general public. Through its “Trends” site, scholars can analyze topics such as interest in Trump, views about democracy, and perspectives on the overall economy. 52 That helps people track movements in public interest and identify topics that galvanize the general public.

Twitter makes much of its tweets available to researchers through application programming interfaces, commonly referred to as APIs. These tools help people outside the company build application software and make use of data from its social media platform. They can study patterns of social media communications and see how people are commenting on or reacting to current events.

In some sectors where there is a discernible public benefit, governments can facilitate collaboration by building infrastructure that shares data. For example, the National Cancer Institute has pioneered a data-sharing protocol where certified researchers can query health data it has using de-identified information drawn from clinical data, claims information, and drug therapies. That enables researchers to evaluate efficacy and effectiveness, and make recommendations regarding the best medical approaches, without compromising the privacy of individual patients.

There could be public-private data partnerships that combine government and business data sets to improve system performance. For example, cities could integrate information from ride-sharing services with its own material on social service locations, bus lines, mass transit, and highway congestion to improve transportation. That would help metropolitan areas deal with traffic tie-ups and assist in highway and mass transit planning.

Some combination of these approaches would improve data access for researchers, the government, and the business community, without impinging on personal privacy. As noted by Ian Buck, the vice president of NVIDIA, “Data is the fuel that drives the AI engine. The federal government has access to vast sources of information. Opening access to that data will help us get insights that will transform the U.S. economy.” 53 Through its Data.gov portal, the federal government already has put over 230,000 data sets into the public domain, and this has propelled innovation and aided improvements in AI and data analytic technologies. 54 The private sector also needs to facilitate research data access so that society can achieve the full benefits of artificial intelligence.

Increase government investment in AI

According to Greg Brockman, the co-founder of OpenAI, the U.S. federal government invests only $1.1 billion in non-classified AI technology. 55 That is far lower than the amount being spent by China or other leading nations in this area of research. That shortfall is noteworthy because the economic payoffs of AI are substantial. In order to boost economic development and social innovation, federal officials need to increase investment in artificial intelligence and data analytics. Higher investment is likely to pay for itself many times over in economic and social benefits. 56

Promote digital education and workforce development

As AI applications accelerate across many sectors, it is vital that we reimagine our educational institutions for a world where AI will be ubiquitous and students need a different kind of training than they currently receive. Right now, many students do not receive instruction in the kinds of skills that will be needed in an AI-dominated landscape. For example, there currently are shortages of data scientists, computer scientists, engineers, coders, and platform developers. These are skills that are in short supply; unless our educational system generates more people with these capabilities, it will limit AI development.

For these reasons, both state and federal governments have been investing in AI human capital. For example, in 2017, the National Science Foundation funded over 6,500 graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education. 57 The goal is to build a larger pipeline of AI and data analytic personnel so that the United States can reap the full advantages of the knowledge revolution.

But there also needs to be substantial changes in the process of learning itself. It is not just technical skills that are needed in an AI world but skills of critical reasoning, collaboration, design, visual display of information, and independent thinking, among others. AI will reconfigure how society and the economy operate, and there needs to be “big picture” thinking on what this will mean for ethics, governance, and societal impact. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas.

One example of new ways to prepare students for a digital future is IBM’s Teacher Advisor program, utilizing Watson’s free online tools to help teachers bring the latest knowledge into the classroom. They enable instructors to develop new lesson plans in STEM and non-STEM fields, find relevant instructional videos, and help students get the most out of the classroom. 58 As such, they are precursors of new educational environments that need to be created.

Create a federal AI advisory committee

Federal officials need to think about how they deal with artificial intelligence. As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology.

In order to move forward in this area, several members of Congress have introduced the “Future of Artificial Intelligence Act,” a bill designed to establish broad policy and legal principles for AI. It proposes the secretary of commerce create a federal advisory committee on the development and implementation of artificial intelligence. The legislation provides a mechanism for the federal government to get advice on ways to promote a “climate of investment and innovation to ensure the global competitiveness of the United States,” “optimize the development of artificial intelligence to address the potential growth, restructuring, or other changes in the United States workforce,” “support the unbiased development and application of artificial intelligence,” and “protect the privacy rights of individuals.” 59

Among the specific questions the committee is asked to address include the following: competitiveness, workforce impact, education, ethics training, data sharing, international cooperation, accountability, machine learning bias, rural impact, government efficiency, investment climate, job impact, bias, and consumer impact. The committee is directed to submit a report to Congress and the administration 540 days after enactment regarding any legislative or administrative action needed on AI.

This legislation is a step in the right direction, although the field is moving so rapidly that we would recommend shortening the reporting timeline from 540 days to 180 days. Waiting nearly two years for a committee report will certainly result in missed opportunities and a lack of action on important issues. Given rapid advances in the field, having a much quicker turnaround time on the committee analysis would be quite beneficial.

Engage with state and local officials

States and localities also are taking action on AI. For example, the New York City Council unanimously passed a bill that directed the mayor to form a taskforce that would “monitor the fairness and validity of algorithms used by municipal agencies.” 60 The city employs algorithms to “determine if a lower bail will be assigned to an indigent defendant, where firehouses are established, student placement for public schools, assessing teacher performance, identifying Medicaid fraud and determine where crime will happen next.” 61

According to the legislation’s developers, city officials want to know how these algorithms work and make sure there is sufficient AI transparency and accountability. In addition, there is concern regarding the fairness and biases of AI algorithms, so the taskforce has been directed to analyze these issues and make recommendations regarding future usage. It is scheduled to report back to the mayor on a range of AI policy, legal, and regulatory issues by late 2019.

Some observers already are worrying that the taskforce won’t go far enough in holding algorithms accountable. For example, Julia Powles of Cornell Tech and New York University argues that the bill originally required companies to make the AI source code available to the public for inspection, and that there be simulations of its decisionmaking using actual data. After criticism of those provisions, however, former Councilman James Vacca dropped the requirements in favor of a task force studying these issues. He and other city officials were concerned that publication of proprietary information on algorithms would slow innovation and make it difficult to find AI vendors who would work with the city. 62 It remains to be seen how this local task force will balance issues of innovation, privacy, and transparency.

Regulate broad objectives more than specific algorithms

The European Union has taken a restrictive stance on these issues of data collection and analysis. 63 It has rules limiting the ability of companies from collecting data on road conditions and mapping street views. Because many of these countries worry that people’s personal information in unencrypted Wi-Fi networks are swept up in overall data collection, the EU has fined technology firms, demanded copies of data, and placed limits on the material collected. 64 This has made it more difficult for technology companies operating there to develop the high-definition maps required for autonomous vehicles.

The GDPR being implemented in Europe place severe restrictions on the use of artificial intelligence and machine learning. According to published guidelines, “Regulations prohibit any automated decision that ‘significantly affects’ EU citizens. This includes techniques that evaluates a person’s ‘performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements.’” 65 In addition, these new rules give citizens the right to review how digital services made specific algorithmic choices affecting people.

By taking a restrictive stance on issues of data collection and analysis, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

If interpreted stringently, these rules will make it difficult for European software designers (and American designers who work with European counterparts) to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements. Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

It makes more sense to think about the broad objectives desired in AI and enact policies that advance them, as opposed to governments trying to crack open the “black boxes” and see exactly how specific algorithms operate. Regulating individual algorithms will limit innovation and make it difficult for companies to make use of artificial intelligence.

Take biases seriously

Bias and discrimination are serious issues for AI. There already have been a number of cases of unfair treatment linked to historic data, and steps need to be undertaken to make sure that does not become prevalent in artificial intelligence. Existing statutes governing discrimination in the physical economy need to be extended to digital platforms. That will help protect consumers and build confidence in these systems as a whole.

For these advances to be widely adopted, more transparency is needed in how AI systems operate. Andrew Burt of Immuta argues, “The key problem confronting predictive analytics is really transparency. We’re in a world where data science operations are taking on increasingly important tasks, and the only thing holding them back is going to be how well the data scientists who train the models can explain what it is their models are doing.” 66

Maintaining mechanisms for human oversight and control

Some individuals have argued that there needs to be avenues for humans to exercise oversight and control of AI systems. For example, Allen Institute for Artificial Intelligence CEO Oren Etzioni argues there should be rules for regulating these systems. First, he says, AI must be governed by all the laws that already have been developed for human behavior, including regulations concerning “cyberbullying, stock manipulation or terrorist threats,” as well as “entrap[ping] people into committing crimes.” Second, he believes that these systems should disclose they are automated systems and not human beings. Third, he states, “An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information.” 67 His rationale is that these tools store so much data that people have to be cognizant of the privacy risks posed by AI.

In the same vein, the IEEE Global Initiative has ethical guidelines for AI and autonomous systems. Its experts suggest that these models be programmed with consideration for widely accepted human norms and rules for behavior. AI algorithms need to take into effect the importance of these norms, how norm conflict can be resolved, and ways these systems can be transparent about norm resolution. Software designs should be programmed for “nondeception” and “honesty,” according to ethics experts. When failures occur, there must be mitigation mechanisms to deal with the consequences. In particular, AI must be sensitive to problems such as bias, discrimination, and fairness. 68

A group of machine learning experts claim it is possible to automate ethical decisionmaking. Using the trolley problem as a moral dilemma, they ask the following question: If an autonomous car goes out of control, should it be programmed to kill its own passengers or the pedestrians who are crossing the street? They devised a “voting-based system” that asked 1.3 million people to assess alternative scenarios, summarized the overall choices, and applied the overall perspective of these individuals to a range of vehicular possibilities. That allowed them to automate ethical decisionmaking in AI algorithms, taking public preferences into account. 69 This procedure, of course, does not reduce the tragedy involved in any kind of fatality, such as seen in the Uber case, but it provides a mechanism to help AI developers incorporate ethical considerations in their planning.

Penalize malicious behavior and promote cybersecurity

As with any emerging technology, it is important to discourage malicious treatment designed to trick software or use it for undesirable ends. 70 This is especially important given the dual-use aspects of AI, where the same tool can be used for beneficial or malicious purposes. The malevolent use of AI exposes individuals and organizations to unnecessary risks and undermines the virtues of the emerging technology. This includes behaviors such as hacking, manipulating algorithms, compromising privacy and confidentiality, or stealing identities. Efforts to hijack AI in order to solicit confidential information should be seriously penalized as a way to deter such actions. 71

In a rapidly changing world with many entities having advanced computing capabilities, there needs to be serious attention devoted to cybersecurity. Countries have to be careful to safeguard their own systems and keep other nations from damaging their security. 72 According to the U.S. Department of Homeland Security, a major American bank receives around 11 million calls a week at its service center. In order to protect its telephony from denial of service attacks, it uses a “machine learning-based policy engine [that] blocks more than 120,000 calls per month based on voice firewall policies including harassing callers, robocalls and potential fraudulent calls.” 73 This represents a way in which machine learning can help defend technology systems from malevolent attacks.

To summarize, the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics. There already are significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decisionmaking, business models, risk mitigation, and system performance. These developments are generating substantial economic and social benefits.

The world is on the cusp of revolutionizing many sectors through artificial intelligence, but the way AI systems are developed need to be better understood due to the major implications these technologies will have for society as a whole.

Yet the manner in which AI systems unfold has major implications for society as a whole. It matters how policy issues are addressed, ethical conflicts are reconciled, legal realities are resolved, and how much transparency is required in AI and data analytic solutions. 74 Human choices about software development affect the way in which decisions are made and the manner in which they are integrated into organizational routines. Exactly how these processes are executed need to be better understood because they will have substantial impact on the general public soon, and for the foreseeable future. AI may well be a revolution in human affairs, and become the single most influential human innovation in history.

Note: We appreciate the research assistance of Grace Gilberg, Jack Karsten, Hillary Schaub, and Kristjan Tomasson on this project.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Support for this publication was generously provided by Amazon. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment. 

John R. Allen is a member of the Board of Advisors of Amida Technology and on the Board of Directors of Spark Cognition. Both companies work in fields discussed in this piece.

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• Portions of this paper draw on Darrell M. West, The Future of Work: Robots, AI, and Automation , Brookings Institution Press, 2018.
• PriceWaterhouseCoopers, “Sizing the Prize: What’s the Real Value of AI for Your Business and How Can You Capitalise?” 2017.
• Dominic Barton, Jonathan Woetzel, Jeongmin Seong, and Qinzheng Tian, “Artificial Intelligence: Implications for China” (New York: McKinsey Global Institute, April 2017), p. 1.
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• John R. Allen and Amir Husain, “On Hyperwar,” Naval Institute Proceedings , July 17, 2017, pp. 30-36.
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• Paul Mozur and John Markoff, “Is China Outsmarting American Artificial Intelligence?” New York Times , May 28, 2017.
• Economist , “America v China: The Battle for Digital Supremacy,” March 15, 2018.
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• Eric Horvitz, “Reflections on the Status and Future of Artificial Intelligence,” Testimony before the U.S. Senate Subcommittee on Space, Science, and Competitiveness, November 30, 2016, p. 5.
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• Simon Denyer, “China’s Watchful Eye,” Washington Post , January 7, 2018.
• Cameron Kerry and Jack Karsten, “Gauging Investment in Self-Driving Cars,” Brookings Institution, October 16, 2017.
• Portions of this section are drawn from Darrell M. West, “Driverless Cars in China, Europe, Japan, Korea, and the United States,” Brookings Institution, September 2016.
• Yuming Ge, Xiaoman Liu, Libo Tang, and Darrell M. West, “Smart Transportation in China and the United States,” Center for Technology Innovation, Brookings Institution, December 2017.
• Peter Holley, “Uber Signs Deal to Buy 24,000 Autonomous Vehicles from Volvo,” Washington Post , November 20, 2017.
• Daisuke Wakabayashi, “Self-Driving Uber Car Kills Pedestrian in Arizona, Where Robots Roam,” New York Times , March 19, 2018.
• Kevin Desouza, Rashmi Krishnamurthy, and Gregory Dawson, “Learning from Public Sector Experimentation with Artificial Intelligence,” TechTank (blog), Brookings Institution, June 23, 2017.
• Boyd Cohen, “The 10 Smartest Cities in North America,” Fast Company , November 14, 2013.
• Teena Maddox, “66% of US Cities Are Investing in Smart City Technology,” TechRepublic , November 6, 2017.
• Osonde Osoba and William Welser IV, “The Risks of Artificial Intelligence to Security and the Future of Work” (Santa Monica, Calif.: RAND Corp., December 2017) (www.rand.org/pubs/perspectives/PE237.html).
• Ibid., p. 7.
• Dominic Barton, Jonathan Woetzel, Jeongmin Seong, and Qinzheng Tian, “Artificial Intelligence: Implications for China” (New York: McKinsey Global Institute, April 2017), p. 7.
• Executive Office of the President, “Preparing for the Future of Artificial Intelligence,” October 2016, pp. 30-31.
• Elaine Glusac, “As Airbnb Grows, So Do Claims of Discrimination,” New York Times , June 21, 2016.
• “Joy Buolamwini,” Bloomberg Businessweek , July 3, 2017, p. 80.
• Mark Purdy and Paul Daugherty, “Why Artificial Intelligence is the Future of Growth,” Accenture, 2016.
• Jon Valant, “Integrating Charter Schools and Choice-Based Education Systems,” Brown Center Chalkboard blog, Brookings Institution, June 23, 2017.
• Tucker, “‘A White Mask Worked Better.’”
• Cliff Kuang, “Can A.I. Be Taught to Explain Itself?” New York Times Magazine , November 21, 2017.
• Yale Law School Information Society Project, “Governing Machine Learning,” September 2017.
• Katie Benner, “Airbnb Vows to Fight Racism, But Its Users Can’t Sue to Prompt Fairness,” New York Times , June 19, 2016.
• Executive Office of the President, “Artificial Intelligence, Automation, and the Economy” and “Preparing for the Future of Artificial Intelligence.”
• Nancy Scolar, “Facebook’s Next Project: American Inequality,” Politico , February 19, 2018.
• Darrell M. West, “What Internet Search Data Reveals about Donald Trump’s First Year in Office,” Brookings Institution policy report, January 17, 2018.
• Ian Buck, “Testimony before the House Committee on Oversight and Government Reform Subcommittee on Information Technology,” February 14, 2018.
• Keith Nakasone, “Testimony before the House Committee on Oversight and Government Reform Subcommittee on Information Technology,” March 7, 2018.
• Greg Brockman, “The Dawn of Artificial Intelligence,” Testimony before U.S. Senate Subcommittee on Space, Science, and Competitiveness, November 30, 2016.
• Amir Khosrowshahi, “Testimony before the House Committee on Oversight and Government Reform Subcommittee on Information Technology,” February 14, 2018.
• James Kurose, “Testimony before the House Committee on Oversight and Government Reform Subcommittee on Information Technology,” March 7, 2018.
• Stephen Noonoo, “Teachers Can Now Use IBM’s Watson to Search for Free Lesson Plans,” EdSurge , September 13, 2017.
• Congress.gov, “H.R. 4625 FUTURE of Artificial Intelligence Act of 2017,” December 12, 2017.
• Elizabeth Zima, “Could New York City’s AI Transparency Bill Be a Model for the Country?” Government Technology , January 4, 2018.
• Julia Powles, “New York City’s Bold, Flawed Attempt to Make Algorithms Accountable,” New Yorker , December 20, 2017.
• Sheera Frenkel, “Tech Giants Brace for Europe’s New Data Privacy Rules,” New York Times , January 28, 2018.
• Claire Miller and Kevin O’Brien, “Germany’s Complicated Relationship with Google Street View,” New York Times , April 23, 2013.
• Cade Metz, “Artificial Intelligence is Setting Up the Internet for a Huge Clash with Europe,” Wired , July 11, 2016.
• Eric Siegel, “Predictive Analytics Interview Series: Andrew Burt,” Predictive Analytics Times , June 14, 2017.
• Oren Etzioni, “How to Regulate Artificial Intelligence,” New York Times , September 1, 2017.
• “Ethical Considerations in Artificial Intelligence and Autonomous Systems,” unpublished paper. IEEE Global Initiative, 2018.
• Ritesh Noothigattu, Snehalkumar Gaikwad, Edmond Awad, Sohan Dsouza, Iyad Rahwan, Pradeep Ravikumar, and Ariel Procaccia, “A Voting-Based System for Ethical Decision Making,” Computers and Society , September 20, 2017 (www.media.mit.edu/publications/a-voting-based-system-for-ethical-decision-making/).
• Miles Brundage, et al., “The Malicious Use of Artificial Intelligence,” University of Oxford unpublished paper, February 2018.
• John Markoff, “As Artificial Intelligence Evolves, So Does Its Criminal Potential,” New York Times, October 24, 2016, p. B3.
• Economist , “The Challenger: Technopolitics,” March 17, 2018.
• Douglas Maughan, “Testimony before the House Committee on Oversight and Government Reform Subcommittee on Information Technology,” March 7, 2018.
• Levi Tillemann and Colin McCormick, “Roadmapping a U.S.-German Agenda for Artificial Intelligence Policy,” New American Foundation, March 2017.

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• v.32(4); Oct-Dec 2020

The impact of artificial intelligence on human society and bioethics

Michael cheng-tek tai.

Department of Medical Sociology and Social Work, College of Medicine, Chung Shan Medical University, Taichung, Taiwan

Artificial intelligence (AI), known by some as the industrial revolution (IR) 4.0, is going to change not only the way we do things, how we relate to others, but also what we know about ourselves. This article will first examine what AI is, discuss its impact on industrial, social, and economic changes on humankind in the 21 st century, and then propose a set of principles for AI bioethics. The IR1.0, the IR of the 18 th century, impelled a huge social change without directly complicating human relationships. Modern AI, however, has a tremendous impact on how we do things and also the ways we relate to one another. Facing this challenge, new principles of AI bioethics must be considered and developed to provide guidelines for the AI technology to observe so that the world will be benefited by the progress of this new intelligence.

W HAT IS ARTIFICIAL INTELLIGENCE ?

Artificial intelligence (AI) has many different definitions; some see it as the created technology that allows computers and machines to function intelligently. Some see it as the machine that replaces human labor to work for men a more effective and speedier result. Others see it as “a system” with the ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation [ 1 ].

Despite the different definitions, the common understanding of AI is that it is associated with machines and computers to help humankind solve problems and facilitate working processes. In short, it is an intelligence designed by humans and demonstrated by machines. The term AI is used to describe these functions of human-made tool that emulates the “cognitive” abilities of the natural intelligence of human minds [ 2 ].

Along with the rapid development of cybernetic technology in recent years, AI has been seen almost in all our life circles, and some of that may no longer be regarded as AI because it is so common in daily life that we are much used to it such as optical character recognition or the Siri (speech interpretation and recognition interface) of information searching equipment on computer [ 3 ].

D IFFERENT TYPES OF ARTIFICIAL INTELLIGENCE

From the functions and abilities provided by AI, we can distinguish two different types. The first is weak AI, also known as narrow AI that is designed to perform a narrow task, such as facial recognition or Internet Siri search or self-driving car. Many currently existing systems that claim to use “AI” are likely operating as a weak AI focusing on a narrowly defined specific function. Although this weak AI seems to be helpful to human living, there are still some think weak AI could be dangerous because weak AI could cause disruptions in the electric grid or may damage nuclear power plants when malfunctioned.

The new development of the long-term goal of many researchers is to create strong AI or artificial general intelligence (AGI) which is the speculative intelligence of a machine that has the capacity to understand or learn any intelligent task human being can, thus assisting human to unravel the confronted problem. While narrow AI may outperform humans such as playing chess or solving equations, but its effect is still weak. AGI, however, could outperform humans at nearly every cognitive task.

Strong AI is a different perception of AI that it can be programmed to actually be a human mind, to be intelligent in whatever it is commanded to attempt, even to have perception, beliefs and other cognitive capacities that are normally only ascribed to humans [ 4 ].

In summary, we can see these different functions of AI [ 5 , 6 ]:

• Automation: What makes a system or process to function automatically
• Machine learning and vision: The science of getting a computer to act through deep learning to predict and analyze, and to see through a camera, analog-to-digital conversion and digital signal processing
• Natural language processing: The processing of human language by a computer program, such as spam detection and converting instantly a language to another to help humans communicate
• Robotics: A field of engineering focusing on the design and manufacturing of cyborgs, the so-called machine man. They are used to perform tasks for human's convenience or something too difficult or dangerous for human to perform and can operate without stopping such as in assembly lines
• Self-driving car: Use a combination of computer vision, image recognition amid deep learning to build automated control in a vehicle.

D O HUMAN-BEINGS REALLY NEED ARTIFICIAL INTELLIGENCE ?

Is AI really needed in human society? It depends. If human opts for a faster and effective way to complete their work and to work constantly without taking a break, yes, it is. However if humankind is satisfied with a natural way of living without excessive desires to conquer the order of nature, it is not. History tells us that human is always looking for something faster, easier, more effective, and convenient to finish the task they work on; therefore, the pressure for further development motivates humankind to look for a new and better way of doing things. Humankind as the homo-sapiens discovered that tools could facilitate many hardships for daily livings and through tools they invented, human could complete the work better, faster, smarter and more effectively. The invention to create new things becomes the incentive of human progress. We enjoy a much easier and more leisurely life today all because of the contribution of technology. The human society has been using the tools since the beginning of civilization, and human progress depends on it. The human kind living in the 21 st century did not have to work as hard as their forefathers in previous times because they have new machines to work for them. It is all good and should be all right for these AI but a warning came in early 20 th century as the human-technology kept developing that Aldous Huxley warned in his book Brave New World that human might step into a world in which we are creating a monster or a super human with the development of genetic technology.

Besides, up-to-dated AI is breaking into healthcare industry too by assisting doctors to diagnose, finding the sources of diseases, suggesting various ways of treatment performing surgery and also predicting if the illness is life-threatening [ 7 ]. A recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot to perform soft-tissue surgery, stitch together a pig's bowel, and the robot finished the job better than a human surgeon, the team claimed [ 8 , 9 ]. It demonstrates robotically-assisted surgery can overcome the limitations of pre-existing minimally-invasive surgical procedures and to enhance the capacities of surgeons performing open surgery.

Above all, we see the high-profile examples of AI including autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays…etc. All these have made human life much easier and convenient that we are so used to them and take them for granted. AI has become indispensable, although it is not absolutely needed without it our world will be in chaos in many ways today.

T HE IMPACT OF ARTIFICIAL INTELLIGENCE ON HUMAN SOCIETY

Negative impact.

Questions have been asked: With the progressive development of AI, human labor will no longer be needed as everything can be done mechanically. Will humans become lazier and eventually degrade to the stage that we return to our primitive form of being? The process of evolution takes eons to develop, so we will not notice the backsliding of humankind. However how about if the AI becomes so powerful that it can program itself to be in charge and disobey the order given by its master, the humankind?

Let us see the negative impact the AI will have on human society [ 10 , 11 ]:

• A huge social change that disrupts the way we live in the human community will occur. Humankind has to be industrious to make their living, but with the service of AI, we can just program the machine to do a thing for us without even lifting a tool. Human closeness will be gradually diminishing as AI will replace the need for people to meet face to face for idea exchange. AI will stand in between people as the personal gathering will no longer be needed for communication
• Unemployment is the next because many works will be replaced by machinery. Today, many automobile assembly lines have been filled with machineries and robots, forcing traditional workers to lose their jobs. Even in supermarket, the store clerks will not be needed anymore as the digital device can take over human labor
• Wealth inequality will be created as the investors of AI will take up the major share of the earnings. The gap between the rich and the poor will be widened. The so-called “M” shape wealth distribution will be more obvious
• New issues surface not only in a social sense but also in AI itself as the AI being trained and learned how to operate the given task can eventually take off to the stage that human has no control, thus creating un-anticipated problems and consequences. It refers to AI's capacity after being loaded with all needed algorithm may automatically function on its own course ignoring the command given by the human controller
• The human masters who create AI may invent something that is racial bias or egocentrically oriented to harm certain people or things. For instance, the United Nations has voted to limit the spread of nucleus power in fear of its indiscriminative use to destroying humankind or targeting on certain races or region to achieve the goal of domination. AI is possible to target certain race or some programmed objects to accomplish the command of destruction by the programmers, thus creating world disaster.

P OSITIVE IMPACT

There are, however, many positive impacts on humans as well, especially in the field of healthcare. AI gives computers the capacity to learn, reason, and apply logic. Scientists, medical researchers, clinicians, mathematicians, and engineers, when working together, can design an AI that is aimed at medical diagnosis and treatments, thus offering reliable and safe systems of health-care delivery. As health professors and medical researchers endeavor to find new and efficient ways of treating diseases, not only the digital computer can assist in analyzing, robotic systems can also be created to do some delicate medical procedures with precision. Here, we see the contribution of AI to health care [ 7 , 11 ]:

Fast and accurate diagnostics

IBM's Watson computer has been used to diagnose with the fascinating result. Loading the data to the computer will instantly get AI's diagnosis. AI can also provide various ways of treatment for physicians to consider. The procedure is something like this: To load the digital results of physical examination to the computer that will consider all possibilities and automatically diagnose whether or not the patient suffers from some deficiencies and illness and even suggest various kinds of available treatment.

Socially therapeutic robots

Pets are recommended to senior citizens to ease their tension and reduce blood pressure, anxiety, loneliness, and increase social interaction. Now cyborgs have been suggested to accompany those lonely old folks, even to help do some house chores. Therapeutic robots and the socially assistive robot technology help improve the quality of life for seniors and physically challenged [ 12 ].

Reduce errors related to human fatigue

Human error at workforce is inevitable and often costly, the greater the level of fatigue, the higher the risk of errors occurring. Al technology, however, does not suffer from fatigue or emotional distraction. It saves errors and can accomplish the duty faster and more accurately.

Artificial intelligence-based surgical contribution

AI-based surgical procedures have been available for people to choose. Although this AI still needs to be operated by the health professionals, it can complete the work with less damage to the body. The da Vinci surgical system, a robotic technology allowing surgeons to perform minimally invasive procedures, is available in most of the hospitals now. These systems enable a degree of precision and accuracy far greater than the procedures done manually. The less invasive the surgery, the less trauma it will occur and less blood loss, less anxiety of the patients.

Improved radiology

The first computed tomography scanners were introduced in 1971. The first magnetic resonance imaging (MRI) scan of the human body took place in 1977. By the early 2000s, cardiac MRI, body MRI, and fetal imaging, became routine. The search continues for new algorithms to detect specific diseases as well as to analyze the results of scans [ 9 ]. All those are the contribution of the technology of AI.

Virtual presence

The virtual presence technology can enable a distant diagnosis of the diseases. The patient does not have to leave his/her bed but using a remote presence robot, doctors can check the patients without actually being there. Health professionals can move around and interact almost as effectively as if they were present. This allows specialists to assist patients who are unable to travel.

S OME CAUTIONS TO BE REMINDED

Despite all the positive promises that AI provides, human experts, however, are still essential and necessary to design, program, and operate the AI from any unpredictable error from occurring. Beth Kindig, a San Francisco-based technology analyst with more than a decade of experience in analyzing private and public technology companies, published a free newsletter indicating that although AI has a potential promise for better medical diagnosis, human experts are still needed to avoid the misclassification of unknown diseases because AI is not omnipotent to solve all problems for human kinds. There are times when AI meets an impasse, and to carry on its mission, it may just proceed indiscriminately, ending in creating more problems. Thus vigilant watch of AI's function cannot be neglected. This reminder is known as physician-in-the-loop [ 13 ].

The question of an ethical AI consequently was brought up by Elizabeth Gibney in her article published in Nature to caution any bias and possible societal harm [ 14 ]. The Neural Information processing Systems (NeurIPS) conference in Vancouver Canada in 2020 brought up the ethical controversies of the application of AI technology, such as in predictive policing or facial recognition, that due to bias algorithms can result in hurting the vulnerable population [ 14 ]. For instance, the NeurIPS can be programmed to target certain race or decree as the probable suspect of crime or trouble makers.

T HE CHALLENGE OF ARTIFICIAL INTELLIGENCE TO BIOETHICS

Artificial intelligence ethics must be developed.

Bioethics is a discipline that focuses on the relationship among living beings. Bioethics accentuates the good and the right in biospheres and can be categorized into at least three areas, the bioethics in health settings that is the relationship between physicians and patients, the bioethics in social settings that is the relationship among humankind and the bioethics in environmental settings that is the relationship between man and nature including animal ethics, land ethics, ecological ethics…etc. All these are concerned about relationships within and among natural existences.

As AI arises, human has a new challenge in terms of establishing a relationship toward something that is not natural in its own right. Bioethics normally discusses the relationship within natural existences, either humankind or his environment, that are parts of natural phenomena. But now men have to deal with something that is human-made, artificial and unnatural, namely AI. Human has created many things yet never has human had to think of how to ethically relate to his own creation. AI by itself is without feeling or personality. AI engineers have realized the importance of giving the AI ability to discern so that it will avoid any deviated activities causing unintended harm. From this perspective, we understand that AI can have a negative impact on humans and society; thus, a bioethics of AI becomes important to make sure that AI will not take off on its own by deviating from its originally designated purpose.

Stephen Hawking warned early in 2014 that the development of full AI could spell the end of the human race. He said that once humans develop AI, it may take off on its own and redesign itself at an ever-increasing rate [ 15 ]. Humans, who are limited by slow biological evolution, could not compete and would be superseded. In his book Superintelligence, Nick Bostrom gives an argument that AI will pose a threat to humankind. He argues that sufficiently intelligent AI can exhibit convergent behavior such as acquiring resources or protecting itself from being shut down, and it might harm humanity [ 16 ].

The question is–do we have to think of bioethics for the human's own created product that bears no bio-vitality? Can a machine have a mind, consciousness, and mental state in exactly the same sense that human beings do? Can a machine be sentient and thus deserve certain rights? Can a machine intentionally cause harm? Regulations must be contemplated as a bioethical mandate for AI production.

Studies have shown that AI can reflect the very prejudices humans have tried to overcome. As AI becomes “truly ubiquitous,” it has a tremendous potential to positively impact all manner of life, from industry to employment to health care and even security. Addressing the risks associated with the technology, Janosch Delcker, Politico Europe's AI correspondent, said: “I don't think AI will ever be free of bias, at least not as long as we stick to machine learning as we know it today,”…. “What's crucially important, I believe, is to recognize that those biases exist and that policymakers try to mitigate them” [ 17 ]. The High-Level Expert Group on AI of the European Union presented Ethics Guidelines for Trustworthy AI in 2019 that suggested AI systems must be accountable, explainable, and unbiased. Three emphases are given:

• Lawful-respecting all applicable laws and regulations
• Ethical-respecting ethical principles and values
• Robust-being adaptive, reliable, fair, and trustworthy from a technical perspective while taking into account its social environment [ 18 ].

Seven requirements are recommended [ 18 ]:

• AI should not trample on human autonomy. People should not be manipulated or coerced by AI systems, and humans should be able to intervene or oversee every decision that the software makes
• AI should be secure and accurate. It should not be easily compromised by external attacks, and it should be reasonably reliable
• Personal data collected by AI systems should be secure and private. It should not be accessible to just anyone, and it should not be easily stolen
• Data and algorithms used to create an AI system should be accessible, and the decisions made by the software should be “understood and traced by human beings.” In other words, operators should be able to explain the decisions their AI systems make
• Services provided by AI should be available to all, regardless of age, gender, race, or other characteristics. Similarly, systems should not be biased along these lines
• AI systems should be sustainable (i.e., they should be ecologically responsible) and “enhance positive social change”
• AI systems should be auditable and covered by existing protections for corporate whistleblowers. The negative impacts of systems should be acknowledged and reported in advance.

From these guidelines, we can suggest that future AI must be equipped with human sensibility or “AI humanities.” To accomplish this, AI researchers, manufacturers, and all industries must bear in mind that technology is to serve not to manipulate humans and his society. Bostrom and Judkowsky listed responsibility, transparency, auditability, incorruptibility, and predictability [ 19 ] as criteria for the computerized society to think about.

S UGGESTED PRINCIPLES FOR ARTIFICIAL INTELLIGENCE BIOETHICS

Nathan Strout, a reporter at Space and Intelligence System at Easter University, USA, reported just recently that the intelligence community is developing its own AI ethics. The Pentagon made announced in February 2020 that it is in the process of adopting principles for using AI as the guidelines for the department to follow while developing new AI tools and AI-enabled technologies. Ben Huebner, chief of the Office of Director of National Intelligence's Civil Liberties, Privacy, and Transparency Office, said that “We're going to need to ensure that we have transparency and accountability in these structures as we use them. They have to be secure and resilient” [ 20 ]. Two themes have been suggested for the AI community to think more about: Explainability and interpretability. Explainability is the concept of understanding how the analytic works, while interpretability is being able to understand a particular result produced by an analytic [ 20 ].

All the principles suggested by scholars for AI bioethics are well-brought-up. I gather from different bioethical principles in all the related fields of bioethics to suggest four principles here for consideration to guide the future development of the AI technology. We however must bear in mind that the main attention should still be placed on human because AI after all has been designed and manufactured by human. AI proceeds to its work according to its algorithm. AI itself cannot empathize nor have the ability to discern good from evil and may commit mistakes in processes. All the ethical quality of AI depends on the human designers; therefore, it is an AI bioethics and at the same time, a trans-bioethics that abridge human and material worlds. Here are the principles:

• Beneficence: Beneficence means doing good, and here it refers to the purpose and functions of AI should benefit the whole human life, society and universe. Any AI that will perform any destructive work on bio-universe, including all life forms, must be avoided and forbidden. The AI scientists must understand that reason of developing this technology has no other purpose but to benefit human society as a whole not for any individual personal gain. It should be altruistic, not egocentric in nature
• Value-upholding: This refers to AI's congruence to social values, in other words, universal values that govern the order of the natural world must be observed. AI cannot elevate to the height above social and moral norms and must be bias-free. The scientific and technological developments must be for the enhancement of human well-being that is the chief value AI must hold dearly as it progresses further
• Lucidity: AI must be transparent without hiding any secret agenda. It has to be easily comprehensible, detectable, incorruptible, and perceivable. AI technology should be made available for public auditing, testing and review, and subject to accountability standards … In high-stakes settings like diagnosing cancer from radiologic images, an algorithm that can't “explain its work” may pose an unacceptable risk. Thus, explainability and interpretability are absolutely required
• Accountability: AI designers and developers must bear in mind they carry a heavy responsibility on their shoulders of the outcome and impact of AI on whole human society and the universe. They must be accountable for whatever they manufacture and create.

C ONCLUSION

AI is here to stay in our world and we must try to enforce the AI bioethics of beneficence, value upholding, lucidity and accountability. Since AI is without a soul as it is, its bioethics must be transcendental to bridge the shortcoming of AI's inability to empathize. AI is a reality of the world. We must take note of what Joseph Weizenbaum, a pioneer of AI, said that we must not let computers make important decisions for us because AI as a machine will never possess human qualities such as compassion and wisdom to morally discern and judge [ 10 ]. Bioethics is not a matter of calculation but a process of conscientization. Although AI designers can up-load all information, data, and programmed to AI to function as a human being, it is still a machine and a tool. AI will always remain as AI without having authentic human feelings and the capacity to commiserate. Therefore, AI technology must be progressed with extreme caution. As Von der Leyen said in White Paper on AI – A European approach to excellence and trust : “AI must serve people, and therefore, AI must always comply with people's rights…. High-risk AI. That potentially interferes with people's rights has to be tested and certified before it reaches our single market” [ 21 ].

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Artificial intelligence and its impact on everyday life

In recent years, artificial intelligence (AI) has woven itself into our daily lives in ways we may not even be aware of. It has become so pervasive that many remain unaware of both its impact and our reliance upon it. 

From morning to night, going about our everyday routines, AI technology drives much of what we do. When we wake, many of us reach for our mobile phone or laptop to start our day. Doing so has become automatic, and integral to how we function in terms of our decision-making, planning and information-seeking.

Once we’ve switched on our devices, we instantly plug into AI functionality such as:

• face ID and image recognition
• social media
• Google search
• digital voice assistants like Apple’s Siri and Amazon’s Alexa
• online banking
• driving aids – route mapping, traffic updates, weather conditions
• leisure downtime – such as Netflix and Amazon for films and programmes

AI touches every aspect of our personal and professional online lives today. Global communication and interconnectivity in business is, and continues to be, a hugely important area. Capitalising on artificial intelligence and data science is essential, and its potential growth trajectory is limitless.

Whilst AI is accepted as almost commonplace, what exactly is it and how did it originate?

What is artificial intelligence?

AI is the intelligence demonstrated by machines, as opposed to the natural intelligence displayed by both animals and humans. 

The human brain is the most complex organ, controlling all functions of the body and interpreting information from the outside world. Its neural networks comprise approximately 86 billion neurons, all woven together by an estimated 100 trillion synapses. Even now, neuroscientists are yet to unravel and understand many of its ramifications and capabilities. 

The human being is constantly evolving and learning; this mirrors how AI functions at its core. Human intelligence, creativity, knowledge, experience and innovation are the drivers for expansion in current, and future, machine intelligence technologies.

When was artificial intelligence invented?

During the Second World War, work by Alan Turing at Bletchley Park on code-breaking German messages heralded a seminal scientific turning point. His groundbreaking work helped develop some of the basics of computer science. 

By the 1950s, Turing posited whether machines could think for themselves. This radical idea, together with the growing implications of machine learning in problem solving, led to many breakthroughs in the field. Research explored the fundamental possibilities of whether machines could be directed and instructed to:

• apply their own ‘intelligence’ in solving problems like humans.

Computer and cognitive scientists, such as Marvin Minsky and John McCarthy, recognised this potential in the 1950s. Their research, which built on Turing’s, fuelled exponential growth in this area.  Attendees at a 1956 workshop, held at Dartmouth College, USA, laid the foundations for what we now consider the field of AI. Recognised as one of the world’s most prestigious academic research universities, many of those present became artificial intelligence leaders and innovators over the coming decades.

In testimony to his groundbreaking research, the Turing Test – in its updated form – is still applied to today’s AI research, and is used to gauge the measure of success of AI development and projects.

This infographic detailing the history of AI offers a useful snapshot of these main events.

How does artificial intelligence work?

AI is built upon acquiring vast amounts of data. This data can then be manipulated to determine knowledge, patterns and insights. The aim is to create and build upon all these blocks, applying the results to new and unfamiliar scenarios.

Such technology relies on advanced machine learning algorithms and extremely high-level programming, datasets, databases and computer architecture. The success of specific tasks is, amongst other things, down to computational thinking, software engineering and a focus on problem solving.

Artificial intelligence comes in many forms, ranging from simple tools like chatbots in customer services applications, through to complex machine learning systems for huge business organisations. The field is vast, incorporating technologies such as:

• Machine Learning (ML) . Using algorithms and statistical models, ML refers to computer systems which are able to learn and adapt without following explicit instructions. In ML, inferences and analysis are discerned in data patterns, split into three main types: supervised, unsupervised and reinforcement learning.
• Narrow AI . This is integral to modern computer systems, referring to those which have been taught, or have learned, to undertake specific tasks without being explicitly programmed to do so. Examples of narrow AI include: virtual assistants on mobile phones, such as those found on Apple iPhone and Android personal assistants on Google Assistant; and recommendation engines which make suggestions based on search or buying history.
• Artificial General Intelligence (AGI). At times, the worlds of science fiction and reality appear to blur. Hypothetically, AGI – exemplified by the robots in programmes such as Westworld, The Matrix, and Star Trek – has come to represent the ability of intelligent machines which understand and learn any task or process usually undertaken by a human being.
• Strong AI. This term is often used interchangeably with AGI. However, some artificial intelligence academics and researchers believe it should apply only once machines achieve sentience or consciousness.
• Natural Language Processing (NLP). This is a challenging area of AI within computer science, as it requires enormous amounts of data. Expert systems and data interpretation are required to teach intelligent machines how to understand the way in which humans write and speak. NLP applications are increasingly used, for example, within healthcare and call centre settings.
• Deepmind. As major technology organisations seek to capture the machine learning market, they are developing cloud services to tap into sectors such as leisure and recreation. For example, Google’s Deepmind has created a computer programme, AlphaGo, to play the board game Go, whereas IBM’s Watson is a super-computer which famously took part in a televised Watson and Jeopardy! Challenge. Using NLP, Watson answered questions with identifiable speech recognition and response, causing a stir in public awareness regarding the potential future of AI.

Artificial intelligence career prospects

Automation, data science and the use of AI will only continue to expand. Forecasts for the data analytics industry up to 2023 predict exponential expansion in the big data gathering sector. In The Global Big Data Analytics Forecast to 2023, Frost and Sullivan project growth at 29.7%, worth a staggering $40.6 billion.

As such, there exists much as-yet-untapped potential, with growing career prospects. Many top employers seek professionals with the skills, expertise and knowledge to propel their organisational aims forward. Career pathways may include:

• Robotics and self-driving /autonomous cars (such as Waymo, Nissan, Renault)
• Healthcare (for instance, multiple applications in genetic sequencing research, treating tumours, and developing tools to speed up diagnoses including Alzheimer’s disease)
• Academia (leading universities in AI research include MIT, Stanford, Harvard and Cambridge)
• Retail (AmazonGo shops and other innovative shopping options)

What is certain is that with every technological shift, new jobs and careers will be created to replace those lost.

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The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

Topics: AI / Machine Learning , Computer Science

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Artificial intelligence is now part of our everyday lives – and its growing power is a double-edged  sword

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Liz Sonenberg has received funding from the Australian Research Council for several projects in the AI domain. She is a member of the AI100 Standing Committee ( https://ai100.stanford.edu/people-0 ) that commissioned the report discussed in this article.

Toby Walsh receives funding from the Australian Research Council for a project in Trustworthy AI. He was one of the 17 members of the AI100 Study Panel that produced the report described in this article.

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A major new report on the state of artificial intelligence (AI) has just been released . Think of it as the AI equivalent of an Intergovernmental Panel on Climate Change report, in that it identifies where AI is at today, and the promise and perils in view.

From language generation and molecular medicine to disinformation and algorithmic bias, AI has begun to permeate every aspect of our lives.

The report argues that we are at an inflection point where researchers and governments must think and act carefully to contain the risks AI presents and make the most of its benefits.

A century-long study of AI

The report comes out of the AI100 project , which aims to study and anticipate the effects of AI rippling out through our lives over the course of the next 100 years.

AI100 produces a new report every five years: the first was published in 2016, and this is the second. As two points define a line, this second report lets us see the direction AI is taking us in.

One of us (Liz Sonenberg) is a member of the standing committee overseeing the AI100 project, and the other (Toby Walsh) was on the study panel that wrote this particular report. Members of the panel came from across the world, with backgrounds in computer science, engineering, law, political science, policy, sociology and economics.

The promises and perils of AI are becoming real

The report highlights the remarkable progress made in AI over the past five years. AI is leaving the laboratory and has entered our lives, having a “real-world impact on people, institutions, and culture”. Read the news on any given day and you’re likely to find multiple stories about some new advance in AI or some new use of AI.

For example, in natural language processing (NLP), computers can now analyse and even generate realistic human language. To demonstrate, we asked Open AI’s GPT-3 system , one of the largest neural networks ever built, to summarise the AI100 report for you. It did a pretty good job, even if the summary confronts our sense of self by being written in the first person:

In the coming decade, I expect that AI will play an increasingly prominent role in the lives of people everywhere. AI-infused services will become more common, and AI will become increasingly embedded in the daily lives of people across the world. I believe that this will bring with it great economic and societal benefits, but that it will also require us to address the many challenges to ensure that the benefits are broadly shared and that people are not marginalised by these new technologies. A key insight of AI research is that it is easier to build things than to understand why they work. However, defining what success looks like for an AI application is not straightforward. For example, the AI systems that are used in healthcare to analyse symptoms, recommend diagnoses, or choose treatments are often far better than anything that could be built by a human, but their success is hard to quantify.

Read more: GPT-3: new AI can write like a human but don't mistake that for thinking – neuroscientist

As a second example of the recent and remarkable progress in AI, consider the latest breakthrough from Google’s DeepMind. AlphaFold is an AI program that provides a huge step forward in our ability to predict how proteins fold.

This will likely lead to major advances in life sciences and medicine, accelerating efforts to understand the building blocks of life and enabling quicker and more sophisticated drug discovery. Most of the planet now knows to their cost how the unique shape of the spike proteins in the SARS-CoV-2 virus are key to its ability to invade our cells, and also to the vaccines developed to combat its deadly progress.

The AI100 report argues that worries about super-intelligent machines and wide-scale job loss from automation are still premature, requiring AI that is far more capable than available today. The main concern the report raises is not malevolent machines of superior intelligence to humans, but incompetent machines of inferior intelligence.

Once again, it’s easy to find in the news real-life stories of risks and threats to our democratic discourse and mental health posed by AI-powered tools. For instance, Facebook uses machine learning to sort its news feed and give each of its 2 billion users an unique but often inflammatory view of the world.

The time to act is now

It’s clear we’re at an inflection point: we need to think seriously and urgently about the downsides and risks the increasing application of AI is revealing. The ever-improving capabilities of AI are a double-edged sword. Harms may be intentional, like deepfake videos, or unintended, like algorithms that reinforce racial and other biases.

AI research has traditionally been undertaken by computer and cognitive scientists. But the challenges being raised by AI today are not just technical. All areas of human inquiry, and especially the social sciences, need to be included in a broad conversation about the future of the field. Minimising negative impacts on society and enhancing the positives requires consideration from across academia and with societal input.

Governments also have a crucial role to play in shaping the development and application of AI. Indeed, governments around the world have begun to consider and address the opportunities and challenges posed by AI. But they remain behind the curve.

A greater investment of time and resources is needed to meet the challenges posed by the rapidly evolving technologies of AI and associated fields. In addition to regulation, governments also need to educate. In an AI-enabled world, our citizens, from the youngest to the oldest, need to be literate in these new digital technologies.

At the end of the day, the success of AI research will be measured by how it has empowered all people, helping tackle the many wicked problems facing the planet, from the climate emergency to increasing inequality within and between countries.

AI will have failed if it harms or devalues the very people we are trying to help.

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The field of artificial intelligence has made remarkable progress in the past five years and is having real-world impact on people, institutions and culture. The ability of computer programs to perform sophisticated language- and image-processing tasks, core problems that have driven the field since its birth in the 1950s, has advanced significantly. Although the current state of AI technology is still far short of the field’s founding aspiration of recreating full human-like intelligence in machines, research and development teams are leveraging these advances and incorporating them into society-facing applications. For example, the use of AI techniques in healthcare is becoming a reality, and the brain sciences are both a beneficiary of and a contributor to AI advances. Old and new companies are investing money and attention to varying degrees to find ways to build on this progress and provide services that scale in unprecedented ways.

The field’s successes have led to an inflection point: It is now urgent to think seriously about the downsides and risks that the broad application of AI is revealing. The increasing capacity to automate decisions at scale is a double-edged sword; intentional deepfakes or simply unaccountable algorithms making mission-critical recommendations can result in people being misled, discriminated against, and even physically harmed. Algorithms trained on historical data are disposed to reinforce and even exacerbate existing biases and inequalities. Whereas AI research has traditionally been the purview of computer scientists and researchers studying cognitive processes, it has become clear that all areas of human inquiry, especially the social sciences, need to be included in a broader conversation about the future of the field. Minimizing the negative impacts on society and enhancing the positive requires more than one-shot technological solutions; keeping AI on track for positive outcomes relevant to society requires ongoing engagement and continual attention.

Looking ahead, a number of important steps need to be taken. Governments play a critical role in shaping the development and application of AI, and they have been rapidly adjusting to acknowledge the importance of the technology to science, economics, and the process of governing itself. But government institutions are still behind the curve, and sustained investment of time and resources will be needed to meet the challenges posed by rapidly evolving technology. In addition to regulating the most influential aspects of AI applications on society, governments need to look ahead to ensure the creation of informed communities. Incorporating understanding of AI concepts and implications into K-12 education is an example of a needed step to help prepare the next generation to live in and contribute to an equitable AI-infused world.

The AI research community itself has a critical role to play in this regard, learning how to share important trends and findings with the public in informative and actionable ways, free of hype and clear about the dangers and unintended consequences along with the opportunities and benefits. AI researchers should also recognize that complete autonomy is not the eventual goal for AI systems. Our strength as a species comes from our ability to work together and accomplish more than any of us could alone. AI needs to be incorporated into that community-wide system, with clear lines of communication between human and automated decision-makers. At the end of the day, the success of the field will be measured by how it has empowered all people, not by how efficiently machines devalue the very people we are trying to help.

Cite This Report

Michael L. Littman, Ifeoma Ajunwa, Guy Berger, Craig Boutilier, Morgan Currie, Finale Doshi-Velez, Gillian Hadfield, Michael C. Horowitz, Charles Isbell, Hiroaki Kitano, Karen Levy, Terah Lyons, Melanie Mitchell, Julie Shah, Steven Sloman, Shannon Vallor, and Toby Walsh. "Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report." Stanford University, Stanford, CA, September 2021. Doc:  http://ai100.stanford.edu/2021-report. Accessed: September 16, 2021.

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© 2021 by Stanford University. Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International):  https://creativecommons.org/licenses/by-nd/4.0/ .

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• Artificial Intelligence and the Future of Humans

Experts say the rise of artificial intelligence will make most people better off over the next decade, but many have concerns about how advances in AI will affect what it means to be human, to be productive and to exercise free will

Table of contents.

• 1. Concerns about human agency, evolution and survival
• 2. Solutions to address AI’s anticipated negative impacts
• 3. Improvements ahead: How humans and AI might evolve together in the next decade
• About this canvassing of experts
• Acknowledgments

Digital life is augmenting human capacities and disrupting eons-old human activities. Code-driven systems have spread to more than half of the world’s inhabitants in ambient information and connectivity, offering previously unimagined opportunities and unprecedented threats. As emerging algorithm-driven artificial intelligence (AI) continues to spread, will people be better off than they are today?

Some 979 technology pioneers, innovators, developers, business and policy leaders, researchers and activists answered this question in a canvassing of experts conducted in the summer of 2018.

The experts predicted networked artificial intelligence will amplify human effectiveness but also threaten human autonomy, agency and capabilities. They spoke of the wide-ranging possibilities; that computers might match or even exceed human intelligence and capabilities on tasks such as complex decision-making, reasoning and learning, sophisticated analytics and pattern recognition, visual acuity, speech recognition and language translation. They said “smart” systems in communities, in vehicles, in buildings and utilities, on farms and in business processes will save time, money and lives and offer opportunities for individuals to enjoy a more-customized future.

Many focused their optimistic remarks on health care and the many possible applications of AI in diagnosing and treating patients or helping senior citizens live fuller and healthier lives. They were also enthusiastic about AI’s role in contributing to broad public-health programs built around massive amounts of data that may be captured in the coming years about everything from personal genomes to nutrition. Additionally, a number of these experts predicted that AI would abet long-anticipated changes in formal and informal education systems.

Yet, most experts, regardless of whether they are optimistic or not, expressed concerns about the long-term impact of these new tools on the essential elements of being human. All respondents in this non-scientific canvassing were asked to elaborate on why they felt AI would leave people better off or not. Many shared deep worries, and many also suggested pathways toward solutions. The main themes they sounded about threats and remedies are outlined in the accompanying table.

[chart id=”21972″]

Specifically, participants were asked to consider the following:

“Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties.

Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today?”

Overall, and despite the downsides they fear, 63% of respondents in this canvassing said they are hopeful that most individuals will be mostly better off in 2030, and 37% said people will not be better off.

A number of the thought leaders who participated in this canvassing said humans’ expanding reliance on technological systems will only go well if close attention is paid to how these tools, platforms and networks are engineered, distributed and updated. Some of the powerful, overarching answers included those from:

Sonia Katyal , co-director of the Berkeley Center for Law and Technology and a member of the inaugural U.S. Commerce Department Digital Economy Board of Advisors, predicted, “In 2030, the greatest set of questions will involve how perceptions of AI and their application will influence the trajectory of civil rights in the future. Questions about privacy, speech, the right of assembly and technological construction of personhood will all re-emerge in this new AI context, throwing into question our deepest-held beliefs about equality and opportunity for all. Who will benefit and who will be disadvantaged in this new world depends on how broadly we analyze these questions today, for the future.”

We need to work aggressively to make sure technology matches our values. Erik Brynjolfsson

[machine learning]

Bryan Johnson , founder and CEO of Kernel, a leading developer of advanced neural interfaces, and OS Fund, a venture capital firm, said, “I strongly believe the answer depends on whether we can shift our economic systems toward prioritizing radical human improvement and staunching the trend toward human irrelevance in the face of AI. I don’t mean just jobs; I mean true, existential irrelevance, which is the end result of not prioritizing human well-being and cognition.”

Andrew McLaughlin , executive director of the Center for Innovative Thinking at Yale University, previously deputy chief technology officer of the United States for President Barack Obama and global public policy lead for Google, wrote, “2030 is not far in the future. My sense is that innovations like the internet and networked AI have massive short-term benefits, along with long-term negatives that can take decades to be recognizable. AI will drive a vast range of efficiency optimizations but also enable hidden discrimination and arbitrary penalization of individuals in areas like insurance, job seeking and performance assessment.”

Michael M. Roberts , first president and CEO of the Internet Corporation for Assigned Names and Numbers (ICANN) and Internet Hall of Fame member, wrote, “The range of opportunities for intelligent agents to augment human intelligence is still virtually unlimited. The major issue is that the more convenient an agent is, the more it needs to know about you – preferences, timing, capacities, etc. – which creates a tradeoff of more help requires more intrusion. This is not a black-and-white issue – the shades of gray and associated remedies will be argued endlessly. The record to date is that convenience overwhelms privacy. I suspect that will continue.”

danah boyd , a principal researcher for Microsoft and founder and president of the Data & Society Research Institute, said, “AI is a tool that will be used by humans for all sorts of purposes, including in the pursuit of power. There will be abuses of power that involve AI, just as there will be advances in science and humanitarian efforts that also involve AI. Unfortunately, there are certain trend lines that are likely to create massive instability. Take, for example, climate change and climate migration. This will further destabilize Europe and the U.S., and I expect that, in panic, we will see AI be used in harmful ways in light of other geopolitical crises.”

Amy Webb , founder of the Future Today Institute and professor of strategic foresight at New York University, commented, “The social safety net structures currently in place in the U.S. and in many other countries around the world weren’t designed for our transition to AI. The transition through AI will last the next 50 years or more. As we move farther into this third era of computing, and as every single industry becomes more deeply entrenched with AI systems, we will need new hybrid-skilled knowledge workers who can operate in jobs that have never needed to exist before. We’ll need farmers who know how to work with big data sets. Oncologists trained as robotocists. Biologists trained as electrical engineers. We won’t need to prepare our workforce just once, with a few changes to the curriculum. As AI matures, we will need a responsive workforce, capable of adapting to new processes, systems and tools every few years. The need for these fields will arise faster than our labor departments, schools and universities are acknowledging. It’s easy to look back on history through the lens of present – and to overlook the social unrest caused by widespread technological unemployment. We need to address a difficult truth that few are willing to utter aloud: AI will eventually cause a large number of people to be permanently out of work. Just as generations before witnessed sweeping changes during and in the aftermath of the Industrial Revolution, the rapid pace of technology will likely mean that Baby Boomers and the oldest members of Gen X – especially those whose jobs can be replicated by robots – won’t be able to retrain for other kinds of work without a significant investment of time and effort.”

Barry Chudakov , founder and principal of Sertain Research, commented, “By 2030 the human-machine/AI collaboration will be a necessary tool to manage and counter the effects of multiple simultaneous accelerations: broad technology advancement, globalization, climate change and attendant global migrations. In the past, human societies managed change through gut and intuition, but as Eric Teller, CEO of Google X, has said, ‘Our societal structures are failing to keep pace with the rate of change.’ To keep pace with that change and to manage a growing list of ‘wicked problems’ by 2030, AI – or using Joi Ito’s phrase, extended intelligence – will value and revalue virtually every area of human behavior and interaction. AI and advancing technologies will change our response framework and time frames (which in turn, changes our sense of time). Where once social interaction happened in places – work, school, church, family environments – social interactions will increasingly happen in continuous, simultaneous time. If we are fortunate, we will follow the 23 Asilomar AI Principles outlined by the Future of Life Institute and will work toward ‘not undirected intelligence but beneficial intelligence.’ Akin to nuclear deterrence stemming from mutually assured destruction, AI and related technology systems constitute a force for a moral renaissance. We must embrace that moral renaissance, or we will face moral conundrums that could bring about human demise. … My greatest hope for human-machine/AI collaboration constitutes a moral and ethical renaissance – we adopt a moonshot mentality and lock arms to prepare for the accelerations coming at us. My greatest fear is that we adopt the logic of our emerging technologies – instant response, isolation behind screens, endless comparison of self-worth, fake self-presentation – without thinking or responding smartly.”

John C. Havens , executive director of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems and the Council on Extended Intelligence, wrote, “Now, in 2018, a majority of people around the world can’t access their data, so any ‘human-AI augmentation’ discussions ignore the critical context of who actually controls people’s information and identity. Soon it will be extremely difficult to identify any autonomous or intelligent systems whose algorithms don’t interact with human data in one form or another.”

At stake is nothing less than what sort of society we want to live in and how we experience our humanity. Batya Friedman

Batya Friedman , a human-computer interaction professor at the University of Washington’s Information School, wrote, “Our scientific and technological capacities have and will continue to far surpass our moral ones – that is our ability to use wisely and humanely the knowledge and tools that we develop. … Automated warfare – when autonomous weapons kill human beings without human engagement – can lead to a lack of responsibility for taking the enemy’s life or even knowledge that an enemy’s life has been taken. At stake is nothing less than what sort of society we want to live in and how we experience our humanity.”

Greg Shannon , chief scientist for the CERT Division at Carnegie Mellon University, said, “Better/worse will appear 4:1 with the long-term ratio 2:1. AI will do well for repetitive work where ‘close’ will be good enough and humans dislike the work. … Life will definitely be better as AI extends lifetimes, from health apps that intelligently ‘nudge’ us to health, to warnings about impending heart/stroke events, to automated health care for the underserved (remote) and those who need extended care (elder care). As to liberty, there are clear risks. AI affects agency by creating entities with meaningful intellectual capabilities for monitoring, enforcing and even punishing individuals. Those who know how to use it will have immense potential power over those who don’t/can’t. Future happiness is really unclear. Some will cede their agency to AI in games, work and community, much like the opioid crisis steals agency today. On the other hand, many will be freed from mundane, unengaging tasks/jobs. If elements of community happiness are part of AI objective functions, then AI could catalyze an explosion of happiness.”

Kostas Alexandridis , author of “Exploring Complex Dynamics in Multi-agent-based Intelligent Systems,” predicted, “Many of our day-to-day decisions will be automated with minimal intervention by the end-user. Autonomy and/or independence will be sacrificed and replaced by convenience. Newer generations of citizens will become more and more dependent on networked AI structures and processes. There are challenges that need to be addressed in terms of critical thinking and heterogeneity. Networked interdependence will, more likely than not, increase our vulnerability to cyberattacks. There is also a real likelihood that there will exist sharper divisions between digital ‘haves’ and ‘have-nots,’ as well as among technologically dependent digital infrastructures. Finally, there is the question of the new ‘commanding heights’ of the digital network infrastructure’s ownership and control.”

Oscar Gandy , emeritus professor of communication at the University of Pennsylvania, responded, “We already face an ungranted assumption when we are asked to imagine human-machine ‘collaboration.’ Interaction is a bit different, but still tainted by the grant of a form of identity – maybe even personhood – to machines that we will use to make our way through all sorts of opportunities and challenges. The problems we will face in the future are quite similar to the problems we currently face when we rely upon ‘others’ (including technological systems, devices and networks) to acquire things we value and avoid those other things (that we might, or might not be aware of).”

James Scofield O’Rourke , a professor of management at the University of Notre Dame, said, “Technology has, throughout recorded history, been a largely neutral concept. The question of its value has always been dependent on its application. For what purpose will AI and other technological advances be used? Everything from gunpowder to internal combustion engines to nuclear fission has been applied in both helpful and destructive ways. Assuming we can contain or control AI (and not the other way around), the answer to whether we’ll be better off depends entirely on us (or our progeny). ‘The fault, dear Brutus, is not in our stars, but in ourselves, that we are underlings.’”

Simon Biggs , a professor of interdisciplinary arts at the University of Edinburgh, said, “AI will function to augment human capabilities. The problem is not with AI but with humans. As a species we are aggressive, competitive and lazy. We are also empathic, community minded and (sometimes) self-sacrificing. We have many other attributes. These will all be amplified. Given historical precedent, one would have to assume it will be our worst qualities that are augmented. My expectation is that in 2030 AI will be in routine use to fight wars and kill people, far more effectively than we can currently kill. As societies we will be less affected by this as we currently are, as we will not be doing the fighting and killing ourselves. Our capacity to modify our behaviour, subject to empathy and an associated ethical framework, will be reduced by the disassociation between our agency and the act of killing. We cannot expect our AI systems to be ethical on our behalf – they won’t be, as they will be designed to kill efficiently, not thoughtfully. My other primary concern is to do with surveillance and control. The advent of China’s Social Credit System (SCS) is an indicator of what it likely to come. We will exist within an SCS as AI constructs hybrid instances of ourselves that may or may not resemble who we are. But our rights and affordances as individuals will be determined by the SCS. This is the Orwellian nightmare realised.”

Mark Surman , executive director of the Mozilla Foundation, responded, “AI will continue to concentrate power and wealth in the hands of a few big monopolies based on the U.S. and China. Most people – and parts of the world – will be worse off.”

William Uricchio , media scholar and professor of comparative media studies at MIT, commented, “AI and its related applications face three problems: development at the speed of Moore’s Law, development in the hands of a technological and economic elite, and development without benefit of an informed or engaged public. The public is reduced to a collective of consumers awaiting the next technology. Whose notion of ‘progress’ will prevail? We have ample evidence of AI being used to drive profits, regardless of implications for long-held values; to enhance governmental control and even score citizens’ ‘social credit’ without input from citizens themselves. Like technologies before it, AI is agnostic. Its deployment rests in the hands of society. But absent an AI-literate public, the decision of how best to deploy AI will fall to special interests. Will this mean equitable deployment, the amelioration of social injustice and AI in the public service? Because the answer to this question is social rather than technological, I’m pessimistic. The fix? We need to develop an AI-literate public, which means focused attention in the educational sector and in public-facing media. We need to assure diversity in the development of AI technologies. And until the public, its elected representatives and their legal and regulatory regimes can get up to speed with these fast-moving developments we need to exercise caution and oversight in AI’s development.”

The remainder of this report is divided into three sections that draw from hundreds of additional respondents’ hopeful and critical observations: 1) concerns about human-AI evolution, 2) suggested solutions to address AI’s impact, and 3) expectations of what life will be like in 2030, including respondents’ positive outlooks on the quality of life and the future of work, health care and education. Some responses are lightly edited for style.

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Artificial Intelligence Essay for Students and Children

500+ words essay on artificial intelligence.

Artificial Intelligence refers to the intelligence of machines. This is in contrast to the natural intelligence of humans and animals. With Artificial Intelligence, machines perform functions such as learning, planning, reasoning and problem-solving. Most noteworthy, Artificial Intelligence is the simulation of human intelligence by machines. It is probably the fastest-growing development in the World of technology and innovation . Furthermore, many experts believe AI could solve major challenges and crisis situations.

Types of Artificial Intelligence

First of all, the categorization of Artificial Intelligence is into four types. Arend Hintze came up with this categorization. The categories are as follows:

Type 1: Reactive machines – These machines can react to situations. A famous example can be Deep Blue, the IBM chess program. Most noteworthy, the chess program won against Garry Kasparov , the popular chess legend. Furthermore, such machines lack memory. These machines certainly cannot use past experiences to inform future ones. It analyses all possible alternatives and chooses the best one.

Type 2: Limited memory – These AI systems are capable of using past experiences to inform future ones. A good example can be self-driving cars. Such cars have decision making systems . The car makes actions like changing lanes. Most noteworthy, these actions come from observations. There is no permanent storage of these observations.

Type 3: Theory of mind – This refers to understand others. Above all, this means to understand that others have their beliefs, intentions, desires, and opinions. However, this type of AI does not exist yet.

Type 4: Self-awareness – This is the highest and most sophisticated level of Artificial Intelligence. Such systems have a sense of self. Furthermore, they have awareness, consciousness, and emotions. Obviously, such type of technology does not yet exist. This technology would certainly be a revolution .

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Applications of Artificial Intelligence

First of all, AI has significant use in healthcare. Companies are trying to develop technologies for quick diagnosis. Artificial Intelligence would efficiently operate on patients without human supervision. Such technological surgeries are already taking place. Another excellent healthcare technology is IBM Watson.

Artificial Intelligence in business would significantly save time and effort. There is an application of robotic automation to human business tasks. Furthermore, Machine learning algorithms help in better serving customers. Chatbots provide immediate response and service to customers.

AI can greatly increase the rate of work in manufacturing. Manufacture of a huge number of products can take place with AI. Furthermore, the entire production process can take place without human intervention. Hence, a lot of time and effort is saved.

Artificial Intelligence has applications in various other fields. These fields can be military , law , video games , government, finance, automotive, audit, art, etc. Hence, it’s clear that AI has a massive amount of different applications.

To sum it up, Artificial Intelligence looks all set to be the future of the World. Experts believe AI would certainly become a part and parcel of human life soon. AI would completely change the way we view our World. With Artificial Intelligence, the future seems intriguing and exciting.

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The Role of Artificial Intelligence in Modern Society – An In-Depth Exploration of AI’s Impact and Future Implications

In today’s rapidly advancing world, technology has become an integral part of our lives. One of the most remarkable innovations in recent years is the development of artificial intelligence (AI). AI, often referred to as machine learning, has the ability to analyze vast amounts of data and make predictions or decisions based on patterns it detects. This essay will delve into the impact of artificial intelligence, examining its potential benefits, challenges, and ethical implications.

The field of artificial intelligence has made significant strides in recent years, with experts predicting that AI will have a profound impact on various industries. One of the major benefits of AI lies in its ability to automate tasks, freeing up valuable time and resources. For example, AI-powered algorithms can be used to analyze large datasets and identify trends or patterns that may not be immediately apparent to human analysts. This can greatly enhance productivity and efficiency in fields such as finance, healthcare, and marketing.

However, the rapid advancement of artificial intelligence also poses challenges and raises important ethical questions. As AI becomes more prevalent, concerns about job displacement and the future of work arise. Some argue that AI will lead to widespread automation, resulting in job losses in certain sectors. While this may be true to some extent, it is important to note that AI also has the potential to create new job opportunities. As certain tasks become automated, individuals can focus on more complex and creative tasks that require human ingenuity.

Another ethical concern associated with AI is the issue of bias. AI algorithms are trained on vast amounts of data, which can sometimes contain inherent biases. If these biases are not properly addressed, AI systems can perpetuate and even amplify existing inequalities. For example, facial recognition technology has been found to be less accurate in identifying people of color, leading to potential discrimination in fields such as law enforcement and hiring. It is crucial that AI developers and policymakers work together to ensure that AI systems are fair and unbiased.

In conclusion, artificial intelligence has the potential to revolutionize numerous aspects of our lives. Its ability to automate tasks and analyze vast amounts of data has the power to enhance productivity and efficiency in various industries. However, as AI continues to advance, it is important to address the challenges it presents, such as job displacement and bias. By understanding and mitigating these challenges, we can harness the full potential of AI while ensuring that its impact is positive and equitable.

Understanding Artificial Intelligence

Artificial intelligence (AI) is a technology that has gained significant attention in recent years. With advancements in machine learning and data analysis, AI has become a hot topic in many fields. This essay aims to provide a comprehensive understanding of artificial intelligence and its impact on society.

At its core, artificial intelligence refers to the ability of machines to mimic human intelligence and perform tasks that traditionally require human intervention. The term “artificial” signifies the non-natural origin of this intelligence. AI systems are designed to analyze and interpret large amounts of data, learn from patterns, and make predictions or decisions based on this analysis.

One of the key components of artificial intelligence is machine learning, which allows machines to learn from experience and improve their performance over time. Machine learning algorithms enable AI systems to automatically identify patterns, make predictions, and adapt to new data. This ability to learn and adapt is what sets AI apart from traditional computer programs.

Artificial intelligence has the potential to revolutionize various industries, such as healthcare, finance, and transportation. In healthcare, AI algorithms can be used to analyze medical images, diagnose diseases, and develop personalized treatment plans. In finance, AI can be leveraged to analyze market trends, predict stock prices, and optimize investment strategies. In transportation, AI can improve efficiency and safety through autonomous vehicles and intelligent traffic management systems.

However, the widespread adoption of artificial intelligence also raises ethical and societal concerns. There are concerns about the potential for AI systems to be biased, reinforce existing inequalities, or invade privacy. There is also a fear of job displacement as AI systems automate tasks previously performed by humans. It is crucial to address these concerns and develop responsible AI technologies that benefit society as a whole.

In conclusion, artificial intelligence is a rapidly advancing technology that has the potential to transform various aspects of our lives. With its ability to analyze data, learn from patterns, and make predictions, AI has become a powerful tool in many industries. However, it is important to understand the ethical implications and potential risks associated with its implementation. By harnessing the power of artificial intelligence responsibly, we can leverage this technology to create a better future.

Historical Developments in AI

Artificial Intelligence (AI) has a rich history that dates back over 2000 years. The concept of AI, which refers to the simulation of human intelligence in machines, has long captivated the minds of scientists and thinkers throughout history.

Early Concepts of AI

The idea of creating machines that can mimic human intelligence can be traced back to ancient times. Greek myths and stories often featured mechanical robots and creatures that possessed human-like qualities, suggesting early fascination with the concept of artificial intelligence.

However, it was not until the 20th century that significant progress was made in the field of AI. In the 1940s and 1950s, researchers began to explore the idea of building machines that could learn and perform tasks normally requiring human intelligence.

The Birth of AI

The birth of AI as a formal discipline can be attributed to a conference held in Dartmouth College in 1956. At this conference, the first steps towards developing intelligent machines were taken, and the term “artificial intelligence” was coined.

In the following decades, AI research progressed at a rapid pace. Early AI systems focused on rule-based expert systems, which used a set of predefined rules to make decisions or solve problems. These systems were limited in their capabilities and required extensive human input to function effectively.

Advancements in computing technology played a crucial role in the development of AI. The introduction of faster processors and increased storage capacity allowed for the creation of more sophisticated AI systems. This led to the emergence of machine learning algorithms, which enabled machines to learn and improve their performance over time.

The Rise of Automation and Big Data

With the advent of automation and the abundance of data in the digital age, AI has experienced exponential growth. Machine learning algorithms can now process massive amounts of data and make informed decisions without explicit programming.

The rise of big data has also contributed to the advancement of AI. The availability of vast amounts of data has allowed AI systems to train on diverse datasets, enabling them to recognize patterns, make predictions, and perform tasks with a high degree of accuracy.

Today, AI is integrated into various aspects of our lives, from virtual assistants on our smartphones to self-driving cars. The continuous development of AI technology holds the promise of further advancements in automation, healthcare, and other sectors.

• Advancements in computing technology have paved the way for the development of AI.
• Machine learning algorithms have enabled machines to learn and improve their performance over time.
• The abundance of data in the digital age has fueled the growth of AI.
• AI is now integrated into various aspects of our lives and holds the promise of further advancements.

Impact of AI on Business

Artificial intelligence (AI) has had a profound impact on various aspects of business. From automation to machine learning, AI technology has revolutionized the way businesses operate and make decisions.

Increased Efficiency and Productivity

One of the major impacts of AI on business is the increased efficiency and productivity it brings. With AI-powered automation, businesses can streamline their operations and reduce the need for manual intervention. This allows for faster and more accurate processing of tasks, leading to improved productivity and cost savings.

Data Analysis and Decision Making

AI technology has also transformed the way businesses analyze and make decisions based on data. With machine learning algorithms, AI can process and analyze large amounts of data in real-time, uncovering valuable insights and patterns that would be difficult for humans to identify. This enables businesses to make more informed and data-driven decisions.

• Effective Customer Service

AI has also revolutionized customer service in businesses. Chatbots and virtual assistants powered by AI can provide instant and personalized support to customers, resolving their queries and issues in a timely manner. This improves customer satisfaction and helps businesses build better relationships with their customers.

• Enhanced Marketing and Sales

AI technology has also had a significant impact on marketing and sales strategies. By analyzing customer data and behavior, AI can help businesses identify target audiences and create personalized marketing campaigns. This leads to more effective marketing efforts and increased sales conversions.

• Improved Risk Management

AI technology has proven to be a valuable tool for businesses in managing risks. By analyzing data and identifying potential risks, AI can help businesses develop effective risk management strategies. This enables businesses to mitigate risks and improve their overall resilience.

In conclusion, AI technology has had a transformative impact on various aspects of business. From increased efficiency and productivity to improved decision making and customer service, AI has revolutionized the way businesses operate. As AI continues to advance, the impact on business is likely to grow, further shaping the future of work and innovation.

AI in Healthcare

Artificial intelligence (AI) has become a game-changer in the healthcare industry. With its ability to process vast amounts of data and learn from it, AI has revolutionized the way healthcare professionals diagnose, treat, and manage diseases.

Enhancing Diagnosis and Treatment

AI is capable of analyzing medical images and identifying patterns and anomalies that may be difficult for human eyes to detect. This technology has proven to be particularly effective in the early detection of diseases such as cancer. By accurately diagnosing these diseases at an early stage, healthcare providers can initiate treatment sooner, greatly increasing the chances of a successful outcome.

Furthermore, AI-powered systems can provide personalized treatment plans by analyzing patients’ medical records, genetic information, and treatment outcomes. This individualized approach helps healthcare professionals deliver more effective treatments and improve patient outcomes.

Automation and Efficiency

In addition to enhancing diagnosis and treatment, AI also brings automation and efficiency to healthcare systems. With the ability to process large volumes of data quickly and accurately, AI algorithms can assist healthcare professionals in making informed decisions more efficiently.

For example, AI can analyze electronic health records, historical treatment data, and clinical trial results to provide evidence-based treatment recommendations. This automation reduces the time and effort required for healthcare professionals to research and analyze information, allowing them to focus more on patient care.

AI also plays a vital role in streamlining administrative processes such as appointment scheduling, billing, and medical coding. By automating these tasks, healthcare providers can free up valuable time and resources, enabling them to focus on delivering quality care to patients.

In conclusion, the integration of artificial intelligence in healthcare has the potential to revolutionize the industry. From enhancing diagnosis and treatment to automating administrative processes, AI offers numerous benefits that improve patient outcomes and increase efficiency in healthcare systems. As technology continues to advance, we can expect AI to play an even more significant role in the future of healthcare.

AI in Education

Artificial Intelligence (AI) has the potential to revolutionize the field of education. With its ability to process vast amounts of data and perform complex tasks, AI can greatly enhance the learning experience for students and teachers alike. In this essay, we will explore the various ways in which AI can be used in education and the potential impact it could have.

1. Personalized Learning

One of the key benefits of AI in education is its ability to provide personalized learning experiences. By analyzing students’ data and learning patterns, AI algorithms can create individualized lesson plans and adapt the pace and content to suit each student’s needs. This not only helps students learn at their own pace but also allows teachers to focus on areas where students need more assistance.

2. Intelligent Tutoring

AI-powered intelligent tutoring systems can act as virtual tutors, providing personalized guidance and support to students. These systems can monitor students’ progress, identify areas of weakness, and provide targeted feedback and additional resources. This not only helps students improve their understanding and performance but also allows teachers to allocate their time and resources more effectively.

Furthermore, AI-enabled tutoring systems can offer additional support to students with special needs or learning disabilities. These systems can adapt to each student’s unique learning style and provide tailored interventions and accommodations.

3. Automation of Administrative Tasks

Another area where AI can have a significant impact is the automation of administrative tasks. From grading papers to managing student records and schedules, AI systems can streamline administrative processes and free up teachers’ time for more important activities, such as lesson planning and individualized instruction. This automation not only improves efficiency but also reduces the risk of human errors and allows teachers to focus on what they do best – teaching.

4. Virtual Reality and Simulations

AI can also enhance learning experiences through the use of virtual reality (VR) and simulations. VR technology can transport students to different environments and allow them to explore and interact with complex concepts in a hands-on manner. Simulations, on the other hand, can provide realistic scenarios for students to practice and apply their knowledge. These immersive experiences can make learning more engaging and memorable, leading to deeper understanding and retention of information.

The integration of AI in education holds great promise for the future. From personalized learning to intelligent tutoring, automation of administrative tasks, and the use of VR and simulations, AI has the potential to transform education and improve learning outcomes. However, it is important to ensure that AI is used ethically and responsibly, with a focus on equity and inclusivity. As AI continues to advance, it is crucial for educators and policymakers to stay informed and adapt their practices to harness the full potential of AI in education.

AI in Finance

The impact of artificial intelligence in the world of finance has been significant in recent years. With the growing adoption of AI and machine learning technologies, the financial industry has experienced a transformation in the way it operates.

Artificial intelligence, with its ability to process large amounts of data and analyze patterns, has revolutionized the way financial institutions make decisions. The use of AI algorithms has enabled financial institutions to automate various processes, reducing human errors and increasing efficiency.

One of the key areas where AI has made a significant impact in finance is in trading. AI-powered algorithms are used to analyze market trends and make predictions on stock prices, helping traders make informed investment decisions. These algorithms can process vast amounts of data and identify patterns that may go unnoticed by human traders. As a result, AI has become an important tool for traders looking to gain an edge in the market.

In addition to trading, AI has also been leveraged in finance for tasks such as risk assessment, fraud detection, and customer service. AI-powered chatbots have become increasingly popular in customer service, as they can quickly provide responses and assist customers with their queries. This not only improves customer satisfaction but also reduces the burden on human agents.

The automation of certain tasks using AI has also led to increased efficiency in financial processes. For example, AI algorithms can be used to automate credit assessment and loan approval, reducing the time and effort required for these processes. This allows financial institutions to serve customers faster and more efficiently.

Challenges and Future Outlook

While AI has brought many benefits to the finance industry, there are also challenges that need to be addressed. One of the main concerns is the potential for AI algorithms to make biased decisions, as they learn from historical data that may contain inherent biases. To mitigate this risk, it is important to develop algorithms that are fair and unbiased.

Looking ahead, the future of AI in finance seems promising. As technology continues to evolve, we can expect to see more advanced AI-powered solutions that can handle complex financial tasks. However, it is important to find the right balance between automation and human judgment, as some tasks may still require a human touch.

In conclusion, the impact of artificial intelligence in the finance industry has been significant. AI has brought automation, efficiency, and improved decision-making to financial processes. While there are challenges to overcome, the future looks promising with the continued development of AI-powered solutions that can revolutionize the industry.

AI in Transportation

Artificial Intelligence (AI) has been revolutionizing various industries, and transportation is no exception. With advancements in technology, the integration of AI has paved the way for more efficient and automated transportation systems. In this essay, we will explore the impact of AI on transportation and its potential for the future.

One of the key benefits of AI in transportation is automation. AI technologies, such as machine learning algorithms, enable vehicles to operate autonomously. This eliminates the need for human drivers and greatly reduces the risk of human error. Automated vehicles equipped with AI technology can navigate roads, interpret traffic signals, and avoid obstacles, leading to safer and more efficient transportation systems.

By collecting and analyzing vast amounts of data, AI systems can optimize transportation routes and schedules. For instance, machine learning algorithms can analyze real-time traffic data to identify the most efficient routes for vehicles. This not only reduces travel time for passengers but also helps alleviate traffic congestion, making transportation more seamless and environmentally friendly.

The Future of AI in Transportation

As technology continues to advance, the role of AI in transportation is only expected to grow. The development of self-driving cars is a significant milestone in this regard. Self-driving cars equipped with AI algorithms have the potential to transform the way we travel. They can improve road safety by reducing human errors, enhance fuel efficiency, and provide mobility solutions for individuals who are unable to drive.

Furthermore, AI can revolutionize public transportation systems. AI algorithms can analyze data from various sources, such as public transport schedules, passenger counts, and weather conditions, to optimize the efficiency and reliability of public transportation. This can lead to better utilization of resources and improved transportation services for commuters.

In conclusion, the integration of AI in transportation brings numerous benefits, including automation, efficiency, and safety. The future holds even more possibilities for AI in transportation, with the development of self-driving cars and optimized public transportation systems. As we continue to explore the potential of AI technology, it is vital to ensure that it is implemented responsibly and ethically to maximize its benefits for society.

AI in Manufacturing

Artificial Intelligence (AI) is revolutionizing the manufacturing industry, bringing unparalleled advancements in productivity, efficiency, and quality control. With the power of machine learning and automation, AI technology is transforming traditional manufacturing processes.

In the past, manufacturing operations heavily relied on human intervention, which introduced errors and inefficiencies. However, with the integration of AI systems, manufacturers can automate repetitive tasks, resulting in increased accuracy and reduced human error. AI-powered machines can perform complex tasks with precision, leading to higher production rates and improved product quality.

One of the key applications of AI in manufacturing is predictive maintenance. By continuously monitoring equipment and analyzing data, AI algorithms can detect potential failures before they occur. This proactive approach allows manufacturers to schedule maintenance activities, preventing costly downtime and optimizing the lifespan of machinery.

Improved Supply Chain Management

AI technology also plays a vital role in supply chain management. Through the analysis of vast amounts of data, AI algorithms can optimize inventory management, demand forecasting, and logistics. Manufacturers can better predict consumer demand, streamline production schedules, and reduce stockouts or overstock situations. AI algorithms can also identify patterns in customer behavior, enabling manufacturers to respond quickly to changing market trends.

Enhanced Worker Safety

Artificial intelligence is not only transforming manufacturing processes but also improving worker safety. AI-powered robots can perform hazardous tasks that previously put human workers at risk. These robots can handle dangerous materials, work in extreme conditions, and perform repetitive tasks without risking injury or fatigue. This frees up human workers to focus on more complex and creative tasks, ultimately enhancing overall productivity and job satisfaction.

In conclusion, AI is reshaping the manufacturing industry by introducing automation, machine learning, and predictive capabilities. With these advancements, manufacturers can achieve increased productivity, improved production quality, and streamlined supply chain management. AI technology also enhances worker safety, allowing humans and machines to work together seamlessly. As AI continues to evolve, its impacts on manufacturing are expected to grow exponentially in the coming years.

AI in Entertainment

Artificial intelligence (AI) has had a significant impact on the entertainment industry. With advancements in machine learning and automation technology, AI has revolutionized the way we create, consume, and interact with entertainment content.

One area where AI has made a notable impact is in personalized recommendations. Streaming platforms like Netflix and Spotify use AI algorithms to analyze user data and provide personalized content recommendations. By analyzing user preferences, AI technology can suggest movies, TV shows, songs, and artists that align with an individual’s tastes and preferences. This has greatly improved the user experience and has led to increased engagement and satisfaction.

In addition to personalized recommendations, AI has also enabled the creation of realistic virtual characters in video games and movies. AI-powered animation technology can simulate human-like movements and behaviors, making virtual characters more lifelike and immersive. This has enhanced the storytelling experience and has opened up new possibilities for creating unique and engaging content.

AI has also played a crucial role in the automation of certain aspects of entertainment production. For example, AI-powered editing software can automatically analyze raw footage and select the best shots, saving time and effort for editors. Similarly, AI can assist in music composition and production, helping artists create innovative and catchy tunes.

Additive manufacturing, or 3D printing, is another area where AI has made an impact in the entertainment industry. AI algorithms can analyze digital designs and optimize them for 3D printing, resulting in more efficient and cost-effective production. This has allowed filmmakers and game developers to bring their ideas to life in a shorter span of time and at a lower cost.

In conclusion, AI has had a transformative effect on the entertainment industry. From personalized recommendations to realistic virtual characters and automation of production processes, AI has revolutionized the way we consume and create entertainment content. As technology continues to advance, we can expect further innovations in this field, leading to even more immersive and engaging entertainment experiences.

Ethical Considerations with AI

Artificial intelligence is rapidly advancing in today’s technology-driven world. As AI continues to improve and evolve, it is crucial to consider the ethical implications that come with this powerful form of technology. In this essay, we will explore some of the key ethical considerations related to AI.

1. Transparency and Accountability

One major concern with AI is the lack of transparency in its decision-making process. Machine learning algorithms are often opaque, making it difficult to understand how a particular decision was reached. This lack of transparency raises questions about the accountability of AI systems.

It is crucial to ensure that AI systems are designed in a way that allows for transparency and explanation. By making AI systems more transparent, we can better understand the decisions they make and ensure that they are fair and unbiased.

2. Privacy and Data Protection

Another important ethical consideration with AI is the protection of individual privacy and data. AI systems often require large amounts of data to operate effectively. This data can include personal and sensitive information, raising concerns about privacy and data protection.

It is essential to establish strong regulations and safeguards for the collection, storage, and use of data in AI systems. Additionally, individuals must have control over their personal data and be informed of how it is being used by AI systems. This will help protect privacy and ensure that AI is used responsibly.

In conclusion, as AI continues to advance, it is crucial to consider the ethical implications that arise. Transparency and accountability, as well as privacy and data protection, are just some of the key ethical considerations that need to be addressed. By addressing these considerations, we can harness the power of AI technology while ensuring that it is used responsibly and ethically.

AI and Job Market

With the rapid advancement of artificial intelligence (AI) technology, the job market is experiencing significant changes. These changes have both positive and negative impacts on different sectors of the economy.

One of the key words associated with AI in the job market is automation. Automation refers to the ability of machines and AI systems to perform tasks and activities that were traditionally done by humans. This results in increased operational efficiency and productivity. However, it also means that certain jobs may become obsolete, leading to unemployment or job displacement in some industries.

The Impact of Machine Learning

Machine learning, a subset of AI, plays a crucial role in the changing job market. Machine learning algorithms are designed to analyze large amounts of data and make predictions or take specific actions based on the patterns and insights derived from the data. This has led to advancements in various domains, such as healthcare, finance, and marketing.

While machine learning technology has created new job opportunities in fields like data analysis and AI development, it has also caused concerns for workers in other sectors. For example, jobs that involve routine tasks and data processing are more susceptible to being replaced by machines. This poses challenges for individuals who have been working in these roles and now need to develop new skills to remain employable.

The Role of AI in the Future Job Market

As AI continues to evolve, there is ongoing debate about its impact on the future job market. Some experts believe that AI will create more jobs than it displaces, as new roles will emerge to support and manage AI systems. These roles may involve training and supervising AI systems, as well as analyzing and interpreting the results generated by these systems.

On the other hand, there are concerns that AI could lead to widespread job loss, particularly in sectors where automation can replace human labor. This raises questions about the need for retraining and upskilling programs to ensure that workers can adapt to the changing job market.

In conclusion, the advent of artificial intelligence has undoubtedly had a significant impact on the job market. While AI brings numerous benefits in terms of increased efficiency and productivity, it also presents challenges in terms of job displacement and unemployment. Therefore, it is crucial for individuals and organizations to adapt to these changes by embracing new technologies and acquiring relevant skills to thrive in the evolving job market.

AI and Privacy

As artificial intelligence (AI) continues to advance, it is transforming various industries and aspects of our lives. From automation to natural language understanding, AI technology has the potential to revolutionize the way we work, communicate, and access information.

However, this advancement in AI technology also raises concerns about privacy rights. With AI systems being able to process and analyze large amounts of data, questions arise about how this data is collected, stored, and used.

Data Collection and Privacy Concerns

One of the primary concerns with AI and privacy revolves around the collection of personal data. AI systems rely on data to learn and make informed decisions. This data can come from various sources, including social media platforms, smart devices, and online interactions.

The collection of personal data raises concerns about how this information is being used and protected. There is a fear that AI technology can be used to track individuals, monitor their behavior, and invade their privacy. Additionally, the potential for data breaches and unauthorized access to personal information is a significant concern.

Protecting Privacy in an AI-Driven World

As AI technology continues to advance, it is essential to establish guidelines and regulations to protect privacy rights. One approach is to implement strict data protection laws that prioritize user consent and provide transparency about data collection and usage.

Furthermore, organizations and developers need to prioritize data security. This includes implementing robust encryption measures, regularly updating security protocols, and conducting thorough assessments to identify and address potential vulnerabilities.

Education and awareness are also vital in protecting privacy in an AI-driven world. Users need to understand the risks and implications of sharing personal data and be aware of their rights and options when it comes to data privacy. Through education, individuals can make informed decisions about the data they share and the platforms they engage with.

In conclusion, while AI has the potential to revolutionize various industries and improve efficiency and productivity, it also raises concerns about privacy. Data collection and usage are key areas of concern, and it is crucial to establish regulations and guidelines to protect privacy rights. By prioritizing data security, implementing strict data protection laws, and promoting education and awareness, we can navigate the AI landscape while safeguarding privacy.

AI and Cybersecurity

As technology continues to advance at an unprecedented pace, the rise of artificial intelligence (AI) has become a prominent topic of discussion. In this essay, we will explore the impact of AI in the field of cybersecurity.

Cybersecurity has become an essential concern in our increasingly digital world. With the rapid growth of technology, the number and complexity of cyber threats have also increased. Traditional methods of security, such as firewalls and antivirus software, are no longer sufficient to protect against advanced and evolving attacks. This is where AI comes into play.

The Role of AI in Cybersecurity

AI has the potential to revolutionize the field of cybersecurity by providing automated and intelligent solutions to identify, detect, and respond to cyber threats. Machine learning, a subset of AI, allows computers to learn from past data and make predictions and decisions without explicit programming.

Cybersecurity analysts and professionals can leverage AI technologies to analyze vast amounts of data and identify patterns, anomalies, and potential threats in real-time. AI-powered systems can detect and respond to attacks faster than humans, reducing the time it takes to detect and mitigate a cyber threat.

Another area where AI can be applied is in the automation of security operations. AI algorithms can perform tasks such as monitoring network activities, identifying vulnerabilities, and managing security incidents. This automation allows cybersecurity teams to focus on more strategic and complex tasks, improving overall efficiency and effectiveness.

Challenges and Concerns

While AI presents significant advancements in cybersecurity, there are also challenges and concerns that need to be addressed. One concern is the potential for AI systems to be fooled or manipulated by cybercriminals. Adversaries could use AI technology to create sophisticated attacks that bypass AI-powered defenses.

Another challenge is the ethical implications of AI in cybersecurity. The use of AI raises questions about privacy, accountability, and transparency. AI algorithms may inadvertently discriminate against certain individuals or groups, leading to unfair treatment or bias.

Furthermore, the ever-evolving nature of cyber threats means that AI-powered systems need to constantly adapt and learn to keep up with new attack techniques. Ongoing research and development are required to ensure that AI systems can effectively detect and respond to emerging threats.

The Future of AI in Cybersecurity

Despite the challenges, the future of AI in cybersecurity looks promising. AI has the potential to enhance security measures, reduce response times, and improve overall protection against cyber threats. Collaboration between AI researchers, cybersecurity experts, and policymakers is essential to address the challenges and build a safe and secure digital ecosystem.

AI and Social Impact

Artificial Intelligence (AI) has become an integral part of our society, impacting various aspects of our lives. Its ability to simulate human learning and decision-making processes has revolutionized the way we interact with technology. In this essay, we will explore the social impact of AI and how it has transformed different sectors.

One area where AI has made significant advancements is in the field of healthcare. Machine learning algorithms can analyze vast amounts of medical data and assist doctors in diagnosing diseases with accuracy and efficiency. This has led to improved patient outcomes and reduced medical errors.

In the business world, AI-powered technologies have transformed the way companies operate. Automation has become a key component of many industries, streamlining processes and increasing productivity. Intelligent software systems can analyze large datasets, identify patterns, and make predictions, allowing businesses to make informed decisions and stay ahead of the competition.

AI has also had a profound impact on the education sector. Intelligent tutoring systems can adapt to individual learning styles and provide personalized instruction to students. This not only enhances the learning experience but also helps identify areas where students may need additional support. Furthermore, AI-powered language processing technologies have improved language learning by providing instant translation and pronunciation feedback.

The social impact of AI is not limited to specific sectors but extends to society as a whole. AI technology has the potential to disrupt existing job markets, leading to concerns about unemployment and income inequality. However, it also presents new opportunities for job creation and economic growth. As AI continues to advance, it is essential to address these societal implications and ensure that the benefits are shared equitably.

In conclusion, AI has had a profound social impact, transforming various sectors and shaping the way we live and work. Its ability to simulate human intelligence and automate processes has revolutionized industries, enhanced education, and improved healthcare outcomes. However, it is crucial to carefully consider and address the social implications of AI to ensure a fair and inclusive future.

AI and Climate Change

Artificial Intelligence (AI) is rapidly becoming a powerful tool in addressing the global issue of climate change. As the world grapples with the environmental challenges of the 21st century, AI presents innovative solutions to combat and mitigate the effects of climate change.

One of the key ways in which AI can contribute to addressing climate change is through its ability to optimize resource management. Machine learning algorithms can analyze vast amounts of data and identify patterns that humans may not be able to recognize. This can help in predicting climate patterns, optimizing energy consumption, and improving forest management.

Additionally, AI can play a crucial role in automation and reducing human error. Automation technologies driven by AI can help streamline manufacturing processes, transportation systems, and other industries that contribute to greenhouse gas emissions. By minimizing human error and improving efficiency, AI-powered automation can make a significant impact on reducing carbon footprints.

AI can also facilitate the transition to renewable energy sources. Through machine learning algorithms, AI can optimize renewable energy generation and storage systems, making them more efficient and affordable. AI can analyze weather patterns and energy demand to ensure that renewable energy sources are utilized to their maximum potential.

Furthermore, AI can be employed in monitoring and predicting the impact of climate change on ecosystems. Machine learning algorithms can analyze data collected from satellite imagery, sensors, and other sources to identify trends and patterns that indicate the health of ecosystems. This information can be used to develop effective conservation strategies and prevent further degradation.

However, it’s important to note that AI cannot solve climate change on its own. It should be seen as a tool that complements human efforts in addressing the issue. Collaboration between experts in the field of AI and climate change is crucial in order to maximize the potential of this technology.

In conclusion, AI has the potential to revolutionize our approach to climate change. Its learning capabilities, automation technologies, and optimization algorithms can help in mitigating the effects of climate change and facilitating the transition to sustainable practices. By harnessing the power of artificial intelligence, we can create a more sustainable and resilient future for our planet.

AI and Data Analytics

Machine intelligence and data analytics are two key areas where artificial intelligence (AI) is making a significant impact. With the advancement of technology, AI has revolutionized the way data is analyzed and utilized across various industries. This essay explores the transformative power of AI in data analytics and its implications for the future.

The Power of Artificial Intelligence in Data Analytics

Artificial intelligence has enabled machines to learn from large amounts of data and make predictions or decisions based on that information. This capability has revolutionized data analytics, allowing businesses to gain valuable insights and make data-driven decisions. Machine learning algorithms, a subset of AI, are capable of processing massive datasets quickly and accurately, uncovering patterns and correlations that may not be immediately apparent to humans.

AI-powered data analytics has become crucial in fields such as marketing, finance, healthcare, and manufacturing, among others. For example, in marketing, AI can analyze massive amounts of customer data and identify trends and patterns to predict consumer behavior. This information enables businesses to tailor their marketing strategies and campaigns to target specific demographics, leading to increased sales and customer satisfaction.

One of the main advantages of AI in data analytics is automation. AI technology can automate repetitive and time-consuming tasks, freeing up human resources to focus on more complex and strategic activities. This not only increases efficiency but also reduces the likelihood of human error, resulting in more accurate and reliable analysis.

AI-powered data analytics tools can process vast amounts of data in a fraction of the time it would take a human expert. This speed offers businesses a competitive advantage, allowing them to make real-time decisions and respond to market changes promptly. Additionally, AI can handle unstructured data, such as social media posts or customer reviews, which would be difficult for humans to analyze manually. By automating the analysis of unstructured data, AI opens up new possibilities for organizations to gain valuable insights and stay ahead of the competition.

The Future of AI and Data Analytics

The field of AI and data analytics is constantly evolving, and the future holds immense potential. As technology continues to advance, AI algorithms will become more sophisticated, capable of handling even larger datasets and making more accurate predictions. This will further enhance decision-making processes and enable organizations to derive deeper insights from their data.

Furthermore, AI will play a crucial role in addressing challenges related to data privacy and security. With the increasing amount of data being generated and shared, AI algorithms will become essential in identifying and mitigating potential risks and breaches. By analyzing patterns and anomalies in data, AI can help safeguard sensitive information and prevent unauthorized access or data leaks.

In conclusion, AI has revolutionized data analytics by enabling machines to process massive datasets and extract valuable insights. The automation and efficiency provided by AI-powered tools have transformed various industries, allowing businesses to make data-driven decisions and gain a competitive advantage. As technology continues to advance, the future of AI and data analytics holds immense potential for further innovation and growth.

AI and Decision Making

Artificial intelligence (AI) has revolutionized the way we make decisions, providing us with powerful tools and insights that were previously unimaginable. With the advancement in technology, AI has greatly automated processes and empowered decision-making processes across various domains.

One of the key areas where AI has made a significant impact is in the field of automation. Machines equipped with AI technology are capable of analyzing large volumes of data and making complex decisions in real time, thereby eliminating the need for human intervention in certain processes. This has not only increased efficiency but also minimized the chances of errors and biases that can arise from human decision-making.

Machine Learning and AI in Decision Making

Machine learning, a subset of AI, has played a vital role in revolutionizing decision-making processes. Through the use of algorithms, machine learning enables computers to learn from data and improve their performance over time. This significantly enhances the accuracy and effectiveness of decision-making systems.

By analyzing historical data, machine learning algorithms can identify patterns and correlations that may not be immediately apparent to humans. This enables businesses and organizations to make data-driven decisions that are based on comprehensive and objective insights. Machine learning algorithms can quickly process vast amounts of data, allowing for real-time decision-making, which is crucial in today’s fast-paced world.

The Benefits and Challenges of AI in Decision Making

The integration of AI in decision making offers numerous benefits. One of the key advantages is the ability to process and analyze vast amounts of data at an unprecedented speed. This allows organizations to gather insights that can drive innovation, improve efficiency, and optimize resource allocation.

AI also provides decision makers with the ability to simulate and predict various scenarios. By running simulations and generating predictions, businesses can better understand the potential outcomes of different decisions, enabling them to make informed choices that align with their goals and objectives.

However, there are also challenges associated with the use of AI in decision making. One major concern is the question of accountability and transparency. As AI algorithms become increasingly complex, it can be difficult to understand how decisions are being made. This raises concerns about potential biases and unfairness in decision-making processes.

Another challenge is the ethical implications of AI-driven decision making. There are concerns about the potential misuse of AI technology, especially in sensitive areas such as healthcare and criminal justice. It is crucial to develop robust frameworks and regulations to ensure ethical decision-making practices and prevent algorithmic biases.

In conclusion, AI has had a profound impact on decision-making processes. By automating tasks and providing powerful data-driven insights, AI has revolutionized the way we make decisions. However, it is essential to address the challenges associated with AI in decision making, including transparency, ethics, and biases, to ensure that AI technology is used responsibly and for the greater benefit of society.

AI and Automation

In this essay, we will explore the impact of artificial intelligence (AI) on automation. With the rapid advancements in technology, AI has become a powerful tool in various industries, transforming the way businesses operate and making tasks more efficient.

AI involves the creation of intelligent machines that can perform tasks that normally require human intelligence. These machines can learn from experience, adjust to new inputs, and perform complex tasks. As a result, industries are increasingly turning to AI to automate various processes and tasks.

One area where AI and automation have had a significant impact is in manufacturing. Robots equipped with AI technology can carry out repetitive tasks with precision and speed, resulting in increased productivity and reduced costs. This has led to the automation of many manufacturing processes, leading to higher levels of efficiency and quality.

Another industry that has been revolutionized by AI and automation is healthcare. AI-powered algorithms can analyze large amounts of medical data, assisting doctors in making accurate diagnoses and developing personalized treatment plans. Automation in healthcare has also increased efficiency in administrative tasks, such as scheduling appointments and managing patient records, allowing medical professionals to focus more on patient care.

Transportation is another sector where AI and automation have had a significant impact. Self-driving cars, powered by AI, have the potential to revolutionize transportation systems by reducing accidents and congestion. These autonomous vehicles can navigate and respond to their surroundings, making transportation safer and more efficient.

While AI and automation have numerous benefits, there are also challenges and concerns associated with their adoption. One concern is the potential impact on the workforce. As more tasks become automated, there is a risk of job displacement for workers whose roles can be easily replaced by AI-powered machines. However, many experts argue that AI will create new job opportunities and transform existing roles, rather than completely eliminating them.

Another challenge is the ethical implications of AI and automation. As machines become more intelligent, questions arise about the responsibility and accountability for their actions. Issues such as algorithmic bias and privacy concerns need to be addressed to ensure that AI is used ethically and in the best interest of society.

In conclusion, AI and automation have had a profound impact on various industries, revolutionizing processes and making tasks more efficient. From manufacturing to healthcare and transportation, AI-powered machines are transforming the way we work and live. While there are challenges and concerns associated with the adoption of AI, its potential to improve productivity, efficiency, and safety cannot be overlooked.

AI and Robotics

The essay explores the impact of artificial intelligence (AI) and robotics in today’s rapidly evolving technological landscape. With the advancements in AI, machines are now capable of performing tasks that were once only possible for humans. This automation has revolutionized various industries, from manufacturing to healthcare, by increasing efficiency and productivity.

Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It encompasses various technologies, including machine learning and deep learning algorithms, natural language processing, and computer vision. These technologies enable machines to analyze vast amounts of data, identify patterns, and make predictions or decisions based on the data.

One of the key areas where AI and robotics have made a significant impact is in automation. By leveraging AI, industries can automate repetitive tasks, reducing the need for human intervention. For example, in manufacturing, robots can be programmed to assemble products, increasing production speed while maintaining consistency and quality. In the healthcare sector, AI-powered robots can assist in surgeries, enabling more precise and minimally invasive procedures.

Another important application of AI and robotics is in the field of autonomous vehicles. AI algorithms enable vehicles to analyze their surroundings, make real-time decisions, and navigate without human intervention. This technology has the potential to revolutionize transportation by improving road safety, reducing traffic congestion, and enabling more efficient use of resources.

However, the widespread adoption of AI and robotics also raises ethical concerns and challenges. There are concerns about job displacement, as machines can replace human workers in various industries. The fear is that AI and automation may lead to unemployment and economic inequality. Additionally, there are ethical considerations regarding privacy, security, and the potential misuse of AI technologies.

Despite these challenges, the impact of AI and robotics on society is undeniable. The technology has the potential to transform various sectors, from healthcare to transportation, and improve the quality of our lives. As AI continues to evolve, it is crucial to address the ethical and societal implications to ensure that the benefits of this technology are realized while minimizing the negative consequences.

AI and Virtual Reality

Artificial intelligence (AI) and virtual reality (VR) are two groundbreaking technologies that have revolutionized various industries. When combined, these technologies have the potential to significantly impact our lives in the future.

AI, with its ability to learn and process vast amounts of data, can enhance the virtual reality experience in several ways. By incorporating AI algorithms, virtual reality platforms can become more interactive and responsive. AI can analyze the user’s movements and behavior in real-time, allowing for personalized and immersive experiences.

Enhancing Immersion

Virtual reality aims to create a simulated environment that feels real to the user. By leveraging AI, this immersive experience can be taken to the next level. AI algorithms can analyze the user’s preferences, interests, and behavior to generate personalized content that resonates with them. This level of customization can greatly enhance the immersion and engagement of the user.

Moreover, AI algorithms can adapt the virtual reality experience in real-time based on the user’s reactions and emotions. By analyzing facial expressions and physiological data, AI can adjust the environment and content to enhance the user’s emotional engagement. This technology opens up new possibilities for gaming, entertainment, and training simulations.

AI-powered virtual reality platforms have the potential to streamline various processes and increase efficiency. For example, in the field of training and education, AI can analyze the performance of trainees and provide real-time feedback and guidance. This can be particularly valuable in industries such as medicine, aviation, and manufacturing.

Furthermore, AI algorithms can automate repetitive tasks in virtual reality environments, allowing users to focus on more complex and creative aspects. This can expedite workflows and improve productivity in fields such as architecture and design.

In conclusion, the integration of AI and virtual reality holds tremendous potential. From enhancing immersion and personalization to automation and efficiency, these technologies can reshape various industries. As AI continues to advance, we can expect even more profound impacts in the years to come.

AI and Augmented Reality

In this essay, we will explore the impact of artificial intelligence (AI) on augmented reality (AR), two cutting-edge technologies that are rapidly transforming various industries.

AR is a technology that overlays virtual information onto the real world, enhancing our perception and interaction with our surroundings. From entertainment and gaming to education and healthcare, AR has found applications in numerous sectors. With the advancements in AI, AR has undergone a significant transformation, becoming more immersive, intelligent, and user-friendly.

One of the key ways AI has contributed to the evolution of AR is through its machine learning capabilities. Machine learning algorithms enable AR systems to understand and interpret the real-world environment more accurately. By analyzing vast amounts of data, AI algorithms can identify objects, recognize patterns, and make real-time decisions, enhancing the overall AR experience.

Furthermore, AI-powered automation has also played a crucial role in augmenting reality. Automation algorithms enable AR systems to perform complex tasks automatically, reducing the need for manual intervention. This automation not only improves efficiency but also reduces the chances of errors and inconsistencies in AR applications.

Additionally, AI has paved the way for more intelligent and interactive AR experiences. With AI algorithms, AR systems can respond intelligently to user inputs, adapt to changing environments, and provide personalized experiences. This intelligence enables AR to seamlessly integrate virtual elements with the real world, creating more immersive and engaging experiences.

Moreover, AI has empowered AR with enhanced object recognition and tracking capabilities. Through computer vision algorithms, AR systems can accurately detect and track objects in real-time, even in complex and dynamic environments. This capability has opened up new possibilities for industries such as retail, manufacturing, and logistics, enabling tasks like inventory management, quality control, and product visualization through AR.

In conclusion, the integration of artificial intelligence with augmented reality has revolutionized the way we interact with and perceive our surroundings. Through machine learning, automation, and intelligent algorithms, AI has made AR more immersive, accurate, and user-friendly. This convergence of technologies opens up new possibilities across various sectors and promises to reshape industries in the future.

AI and Natural Language Processing

One of the most fascinating applications of artificial intelligence (AI) technology is natural language processing. This field focuses on the interaction between machines and human language, allowing computers to understand, interpret, and generate human language.

Machine learning plays a crucial role in natural language processing (NLP) by enabling machines to learn from large amounts of textual data. By analyzing words, sentences, and the context in which they are used, AI algorithms can extract meaning and insights from text.

Words are the building blocks of language, and AI has revolutionized how machines understand and work with words. With advanced algorithms and models, machines can now process vast amounts of text, identify the underlying structure, and extract useful information. This automation of language analysis has numerous applications across various industries.

For example, in the field of customer service, AI-powered chatbots can understand and respond to customer queries, providing quick and accurate solutions. This not only saves time and resources but also improves the overall customer experience.

In the realm of content creation, NLP technology can aid writers in generating content by providing suggestions, checking grammar and style, and even predicting the target audience’s response. This tool allows writers to be more efficient and productive, while maintaining the quality of their work.

Furthermore, AI and NLP can be leveraged in data analysis and research. By analyzing large volumes of text, machines can identify patterns, trends, and sentiments expressed by individuals or groups of people. This information can be used to gain insights into consumer behavior, public opinion, and societal trends.

In conclusion, AI and natural language processing have transformed the way machines interact with human language. The advancements in machine learning and automation have enabled computers to analyze, interpret, and generate text with remarkable accuracy. This technology has immense potential to revolutionize various industries, from customer service to content creation and data analysis.

What is the impact of artificial intelligence on the job market?

Artificial intelligence is expected to have a significant impact on the job market. While it may eliminate certain jobs that can be automated, it will also create new jobs that are centered around AI technologies. The overall effect on the job market remains debatable, with experts expressing different opinions. Some believe that AI will lead to overall job displacement, while others argue that it will only change the nature of work and require workers to acquire new skills.

How has artificial intelligence improved healthcare?

Artificial intelligence has significantly improved healthcare in various ways. It has the potential to analyze large amounts of medical data quickly and accurately, enabling more accurate diagnoses and treatment plans. AI-powered medical imaging systems have also enhanced the accuracy of diagnostics, allowing for the early detection of diseases. Additionally, AI-based devices and applications are being developed to monitor people’s health conditions in real-time, providing personalized care and improving patient outcomes.

What are the ethical concerns surrounding artificial intelligence?

There are several ethical concerns surrounding artificial intelligence. One major concern is the potential for AI systems to reinforce existing biases and discriminate against certain groups of people. There are also concerns about privacy and data security, as AI systems often require access to large amounts of personal data. The possibility of autonomous AI systems making life-and-death decisions, such as in autonomous vehicles, raises questions about accountability and responsibility. Additionally, there are concerns about the impact of AI on job displacement and inequality.

How is artificial intelligence used in the financial industry?

Artificial intelligence is used in various ways in the financial industry. AI algorithms can analyze large amounts of financial data to detect patterns and make predictions, which is valuable for tasks like fraud detection and risk assessment. AI-powered chatbots are also employed in customer service, providing quick and personalized assistance to users. Additionally, AI-based trading systems are used to automate trading decisions, improve investment strategies, and optimize portfolio management.

What are the potential risks of artificial superintelligence?

The potential risks of artificial superintelligence are a topic of significant debate among researchers and experts. One major concern is that if AI systems become significantly more intelligent than humans, they may disregard human values and goals, leading to unintended consequences or even posing a threat to humanity. There are also concerns about the concentration of power, as those who control powerful AI systems may have disproportionate influence. Ensuring the alignment of AI systems with human values and implementing safeguards to prevent misuse are important considerations for addressing these risks.

What is the impact of artificial intelligence?

The impact of artificial intelligence is significant and wide-ranging. AI has the potential to revolutionize industries, improve efficiency, and enhance our daily lives.

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All Student Theses

Artificial intelligence: the impact it has on american society.

Peggy J. Anderson , Governors State University Follow

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Master of Science

Computer Science

First Advisor

Professor Richard Manprisio

Second Advisor

Professor Freddie Kato

Third Advisor

Professor Mohammed Salam

The goal of this paper seeks to look at Artificial Intelligence (AI) influences and impacts on society in the United States. It focuses on the challenges and opportunities of AI, the current state of AI, where AI may advance to in the future, how far AI will go and the way people view it, the positive impact of AI on society, and the breakdown of nine ethical issues in artificial intelligence.

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Anderson, Peggy J., "Artificial Intelligence: The Impact it has on American Society" (2022). All Student Theses . 131. https://opus.govst.edu/theses/131

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Artificial Intelligence: reflection on its complexity and impact on society

As the Artificial Intelligence (AI) based technologies grow increasingly ubiquitous, UNESCO, in partnership with the Ministry of Education, Culture, Sports, Science and Technology of Japan, hosted a roundtable discussion on 11 September 2018, at UNESCO Headquarters in Paris. Moderated by Peter-Paul Verbeek from the Faculty of Philosophy of Twente University (Netherlands), the debate focused on the potential impacts of AI on society.

“AI is one of the most influential technologies of all time. There is almost no area of our existence that is not affected by AI or will not be affected by it in the future.” – Peter-Paul Verbeek

In celebration of the 25 years of reflection on bioethics and ethics of science and technology at UNESCO, the event highlighted the ethical implications and questions raised by AI technologies towards building more inclusive knowledge societies and achieving sustainable development goals. The event was also the first of a series of roundtables to be organized in the upcoming years, with the aim of increasing awareness on the topic by stakeholders and the general public.

Considering the influential nature of AI, the discussion emphasized the need to develop a dialogue within different areas, including the public, on ethical issues to tackle these projected social changes brought about by this technology. Additionally, themes such as accessibility, security, AI supporting human creativity, challenges in designing ethical AI were discussed.

Finalizing the roundtable discussion with a lively interaction with the audience, the event highlighted the importance of ensuring a ethical debate at the global level, purely , in order to ensure that AI serves humanity in the best possible way, in respect to human rights and values.

Guest speakers featured on the roundtable were: Dr. Birna van Riemsdijk, Assistant Professor of Intimate Computing at TU Delft; Prof. Koichi Hori, Professor in the Department of Aeronautics and Astronautics at the University of Tokyo; Prof. Vanessa Nurock, Associate Professor in Political Theory and Ethics, Political Science Department in Paris 8 University.

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Artificial Intelligence and Its Impact on Education Essay

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Introduction

Ai’s impact on education, the impact of ai on teachers, the impact of ai on students, reference list.

Rooted in computer science, Artificial Intelligence (AI) is defined by the development of digital systems that can perform tasks, which are dependent on human intelligence (Rexford, 2018). Interest in the adoption of AI in the education sector started in the 1980s when researchers were exploring the possibilities of adopting robotic technologies in learning (Mikropoulos, 2018). Their mission was to help learners to study conveniently and efficiently. Today, some of the events and impact of AI on the education sector are concentrated in the fields of online learning, task automation, and personalization learning (Chen, Chen and Lin, 2020). The COVID-19 pandemic is a recent news event that has drawn attention to AI and its role in facilitating online learning among other virtual educational programs. This paper seeks to find out the possible impact of artificial intelligence on the education sector from the perspectives of teachers and learners.

Technology has transformed the education sector in unique ways and AI is no exception. As highlighted above, AI is a relatively new area of technological development, which has attracted global interest in academic and teaching circles. Increased awareness of the benefits of AI in the education sector and the integration of high-performance computing systems in administrative work have accelerated the pace of transformation in the field (Fengchun et al. , 2021). This change has affected different facets of learning to the extent that government agencies and companies are looking to replicate the same success in their respective fields (IBM, 2020). However, while the advantages of AI are widely reported in the corporate scene, few people understand its impact on the interactions between students and teachers. This research gap can be filled by understanding the impact of AI on the education sector, as a holistic ecosystem of learning.

As these gaps in education are minimized, AI is contributing to the growth of the education sector. Particularly, it has increased the number of online learning platforms using big data intelligence systems (Chen, Chen and Lin, 2020). This outcome has been achieved by exploiting opportunities in big data analysis to enhance educational outcomes (IBM, 2020). Overall, the positive contributions that AI has had to the education sector mean that it has expanded opportunities for growth and development in the education sector (Rexford, 2018). Therefore, teachers are likely to benefit from increased opportunities for learning and growth that would emerge from the adoption of AI in the education system.

The impact of AI on teachers can be estimated by examining its effects on the learning environment. Some of the positive outcomes that teachers have associated with AI adoption include increased work efficiency, expanded opportunities for career growth, and an improved rate of innovation adoption (Chen, Chen and Lin, 2020). These benefits are achievable because AI makes it possible to automate learning activities. This process gives teachers the freedom to complete supplementary tasks that support their core activities. At the same time, the freedom they enjoy may be used to enhance creativity and innovation in their teaching practice. Despite the positive outcomes of AI adoption in learning, it undermines the relevance of teachers as educators (Fengchun et al., 2021). This concern is shared among educators because the increased reliance on robotics and automation through AI adoption has created conditions for learning to occur without human input. Therefore, there is a risk that teacher participation may be replaced by machine input.

Performance Evaluation emerges as a critical area where teachers can benefit from AI adoption. This outcome is feasible because AI empowers teachers to monitor the behaviors of their learners and the differences in their scores over a specific time (Mikropoulos, 2018). This comparative analysis is achievable using advanced data management techniques in AI-backed performance appraisal systems (Fengchun et al., 2021). Researchers have used these systems to enhance adaptive group formation programs where groups of students are formed based on a balance of the strengths and weaknesses of the members (Live Tiles, 2021). The information collected using AI-backed data analysis techniques can be recalibrated to capture different types of data. For example, teachers have used AI to understand students’ learning patterns and the correlation between these configurations with the individual understanding of learning concepts (Rexford, 2018). Furthermore, advanced biometric techniques in AI have made it possible for teachers to assess their student’s learning attentiveness.

Overall, the contributions of AI to the teaching practice empower teachers to redesign their learning programs to fill the gaps identified in the performance assessments. Employing the capabilities of AI in their teaching programs has also made it possible to personalize their curriculums to empower students to learn more effectively (Live Tiles, 2021). Nonetheless, the benefits of AI to teachers could be undermined by the possibility of job losses due to the replacement of human labor with machines and robots (Gulson et al. , 2018). These fears are yet to materialize but indications suggest that AI adoption may elevate the importance of machines above those of human beings in learning.

The benefits of AI to teachers can be replicated in student learning because learners are recipients of the teaching strategies adopted by teachers. In this regard, AI has created unique benefits for different groups of learners based on the supportive role it plays in the education sector (Fengchun et al., 2021). For example, it has created conditions necessary for the use of virtual reality in learning. This development has created an opportunity for students to learn at their pace (Live Tiles, 2021). Allowing students to learn at their pace has enhanced their learning experiences because of varied learning speeds. The creation of virtual reality using AI learning has played a significant role in promoting equality in learning by adapting to different learning needs (Live Tiles, 2021). For example, it has helped students to better track their performances at home and identify areas of improvement in the process. In this regard, the adoption of AI in learning has allowed for the customization of learning styles to improve students’ attention and involvement in learning.

AI also benefits students by personalizing education activities to suit different learning styles and competencies. In this analysis, AI holds the promise to develop personalized learning at scale by customizing tools and features of learning in contemporary education systems (du Boulay, 2016). Personalized learning offers several benefits to students, including a reduction in learning time, increased levels of engagement with teachers, improved knowledge retention, and increased motivation to study (Fengchun et al., 2021). The presence of these benefits means that AI enriches students’ learning experiences. Furthermore, AI shares the promise of expanding educational opportunities for people who would have otherwise been unable to access learning opportunities. For example, disabled people are unable to access the same quality of education as ordinary students do. Today, technology has made it possible for these underserved learners to access education services.

Based on the findings highlighted above, AI has made it possible to customize education services to suit the needs of unique groups of learners. By extension, AI has made it possible for teachers to select the most appropriate teaching methods to use for these student groups (du Boulay, 2016). Teachers have reported positive outcomes of using AI to meet the needs of these underserved learners (Fengchun et al., 2021). For example, through online learning, some of them have learned to be more patient and tolerant when interacting with disabled students (Fengchun et al., 2021). AI has also made it possible to integrate the educational and curriculum development plans of disabled and mainstream students, thereby standardizing the education outcomes across the divide. Broadly, these statements indicate that the expansion of opportunities via AI adoption has increased access to education services for underserved groups of learners.

Overall, AI holds the promise to solve most educational challenges that affect the world today. UNESCO (2021) affirms this statement by saying that AI can address most problems in learning through innovation. Therefore, there is hope that the adoption of new technology would accelerate the process of streamlining the education sector. This outcome could be achieved by improving the design of AI learning programs to make them more effective in meeting student and teachers’ needs. This contribution to learning will help to maximize the positive impact and minimize the negative effects of AI on both parties.

The findings of this study demonstrate that the application of AI in education has a largely positive impact on students and teachers. The positive effects are summarized as follows: improved access to education for underserved populations improved teaching practices/instructional learning, and enhanced enthusiasm for students to stay in school. Despite the existence of these positive views, negative outcomes have also been highlighted in this paper. They include the potential for job losses, an increase in education inequalities, and the high cost of installing AI systems. These concerns are relevant to the adoption of AI in the education sector but the benefits of integration outweigh them. Therefore, there should be more support given to educational institutions that intend to adopt AI. Overall, this study demonstrates that AI is beneficial to the education sector. It will improve the quality of teaching, help students to understand knowledge quickly, and spread knowledge via the expansion of educational opportunities.

Chen, L., Chen, P. and Lin, Z. (2020) ‘Artificial intelligence in education: a review’, Institute of Electrical and Electronics Engineers Access , 8(1), pp. 75264-75278.

du Boulay, B. (2016) Artificial intelligence as an effective classroom assistant. Institute of Electrical and Electronics Engineers Intelligent Systems , 31(6), pp.76–81.

Fengchun, M. et al. (2021) AI and education: a guide for policymakers . Paris: UNESCO Publishing.

Gulson, K . et al. (2018) Education, work and Australian society in an AI world . Web.

IBM. (2020) Artificial intelligence . Web.

Live Tiles. (2021) 15 pros and 6 cons of artificial intelligence in the classroom . Web.

Mikropoulos, T. A. (2018) Research on e-Learning and ICT in education: technological, pedagogical and instructional perspectives . New York, NY: Springer.

Rexford, J. (2018) The role of education in AI (and vice versa). Web.

Seo, K. et al. (2021) The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education , 18(54), pp. 1-12.

UNESCO. (2021) Artificial intelligence in education . Web.

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• Published: 21 February 2024

The political and social contradictions of the human and online environment in the context of artificial intelligence applications

• Roman Rakowski   ORCID: orcid.org/0000-0002-1150-2618 1 &
• Petra Kowaliková 2  

Humanities and Social Sciences Communications volume  11 , Article number:  289 ( 2024 ) Cite this article

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The aim of the study is a comprehensive view of the topic of social impacts of the use of artificial intelligence and provides a basis for further discussions and research in this important area. It engages in debates on how to ensure that technological advances in artificial intelligence are compatible with democratic values and social justice. The article emphasizes the need for an interdisciplinary approach to exploring the social impacts of AI and calls for collaboration among technical experts, ethicists, lawyers, and social scientists. It underscores the importance of creating appropriate regulations and ethical guidelines for the use of AI to achieve a society that benefits from technological progress while ensuring justice and protecting the rights of individuals. The authors note that the social and political challenges associated with AI are complex and multifactorial, requiring comprehensive analysis and reflective discussion. The main scientific question of the study is the nature of the relationship between AI and society and what scientific approaches should be chosen in order to uncover this situation as best as possible.

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Introduction.

We are currently living in a time of digital transformation often referred to as digital turn or rise of artificial intelligence in the context of the Society 4.0. Originally, Society 4.0 was associated with changes in the industrial and production sectors, with the potential to reshape the entire social sphere, much like previous technological revolutions. However, it is becoming evident that this technological evolution is affecting all levels of society, not just industry. Modern technology has expanded beyond research, development, and manufacturing, permeating public and private life to the extent that it appears to be creating a society centered around the interconnection of technology, people, and Big Data.

The current nature of society is inextricably linked with information and communication technologies. We could therefore speak of living in a time that is largely organized by means of the analysis and processing of Big Data. Digital technologies are anchored in a broad social, political and ideological context—a context which to a substantial extent defines the age we live in (see e.g. Allmer, 2017 ).

This integration of technology, AI, people and data presents new ethical and political challenges and dilemmas in its implementation. On one hand, technologies and AI are radically transforming our environment, and on the other hand, often without us realizing it, they are also reshaping us; it determines our lives. This “digital turn” is currently challenging established dichotomies in modern society, such as subject/object, public/private, consumption/production, mind/body, work/leisure, culture/nature, and more (Chandler and Fuchs, 2019 , p. 2). Now, we can speak of a digital civil society that needs new elaboration and reflection.

Initial excitement about scientific discoveries and innovations is often tempered by concerns about unintended consequences. Obstacles may include regulatory constraints and economic considerations for transitioning new technologies and AI from the laboratory to practical use. It is widely accepted that there exists a gap between technological potential and implementation due to economic, legal, and organizational factors. However, the introduction of technological innovations is typically driven by their presumed benefits for individuals, social groups, or society as a whole. Any potential negative consequences are usually deemed acceptable as long as they do not directly violate established legal or social norms and can be offset by positive effects in the relevant domain. As technology and AI advances rapidly and plays a greater role in society, the impacts on individuals’ lives and social subsystems must be carefully considered (Matochova et al., 2019 , p. 229; Kowalikova et al., 2020 , pp. 631–636).

One of the primary consequences of this transformation is a departure from the traditional material production and services of late capitalism, shifting the focus towards data production. This shift has been extensively analyzed in the context of “digital capitalism,” as presented by Fuchs and Mosco in 2016 . This change in the economic landscape places a significant emphasis on data generated by users, moving the economic sphere from the physical to the virtual realm, impacting individuals’ orientation within the technological sublime.

The virtual world has become the stage for the process of “datification” of the universe. We can speak about datafication of knowledge in general. It subsequently becomes the platform for the commodification of this data, a topic addressed by Mayer-Schönberger and Cukier in 2014 . Data undergo analysis, often utilizing algorithms, artificial intelligence, neural networks, and deep learning, with the objective of introducing new services and business models.

The commodification of data is a process where data becomes a commercial commodity, increasingly prevalent in the digital age. This process begins with the collection of vast amounts of data from various sources, such as social media, online searches, mobile apps, and sensors in smart devices. These data are then analyzed and processed using sophisticated algorithms to extract useful information, such as user preferences, behaviors, and trends. Subsequently, this data is sold or exchanged among businesses for purposes like targeted advertising, product development, customer service improvement, or market trend forecasting. The commodification of data also raises privacy and ethical concerns, as individuals’ personal information becomes a tradeable commodity without their explicit consent. Therefore, while data commodification brings business benefits and innovations, it also requires careful regulation and ethical management to protect individuals’ rights.

From the standpoint of political economics and Critical Theory, this represents a novel phenomenon. In this new digital economic landscape, the central focus is on data and their generation, as discussed by Bridle in 2018 and Ross in 2019. This marks a distinct strategy for capital accumulation through the public sharing of data. However, a dilemma arises: when data are generated by users themselves, it becomes challenging to determine who the actual producer is and who holds ownership rights over the data. These data are often claimed as private property by large corporations, transforming them into information commodities that are rooted in knowledge, ideas, communication, and their broader cultural context, as argued by Keen in 2019 . This problem brings up a big question about the nature of work and the working process. We need a redefined concept of work in general.

Hence, it is imperative to employ a critical approach to unveil the concealed mechanisms behind the processes of digital data commodification. This will enable the formulation of normative principles that establish a legal framework to govern these emerging phenomena (Rakowski and Kowalikova, p. 32, 2020 ). Reactions to the dynamics of ongoing change range from efforts to stabilize the environment through new control mechanisms and increased monitoring to the adoption of change and the restructuring of familiar interpretation frameworks. Some individuals may also experience feelings of helplessness and alienation in the face of these changes (Veitas and Weinbaum, 2017 , pp. 1–2).

The constant emphasis on risks in the public domain, along with efforts to significantly mitigate them, disrupts our sense of ontological security. In advanced societies today, individuals are more likely to face health risks such as overeating rather than famine, suicide rather than physical attacks, and old age rather than infectious diseases (Harari, 2018 , p. 397).

The ongoing discussion regarding the future societal transformation brought about by digital advancements takes into account not just the significant and positive impacts of these technological developments but also the potential adverse outcomes. Novel materials created through these technologies may pose risks to political and social systems. According to certain authors, they might even give rise to global existential threats to human civilization.

There is a phenomenon known as technochauvinism, as described by Broussard in 2018 , which revolves around the belief that technology always represents the optimal solution to any problem and is inherently superior to traditional or non-technological methods. However, this perspective can result in the neglect of non-technological alternatives or the dismissal of valid criticism of technological progress. One of the most significant ways in which technology can contribute to social inequality is through uneven access to technology itself. Even when technology is accessible, individuals lacking the essential skills and training to effectively utilize it may find themselves at a disadvantage. This can lead to the emergence of a digital divide, where disparities in access to technology further widen existing social inequalities.

Furthermore, technology can perpetuate biases and discrimination prevalent in society, and it may also pose threats to individual privacy and civil liberties, especially for marginalized groups who might be subjected to increased scrutiny and surveillance. These issues have the potential to reinforce and solidify preexisting social inequalities.

Hence, it is crucial to delve into the examination of how the unintended consequences, often referred to as externalities, of technological advancements impact society’s well-being. It is imperative to identify the secondary effects of these changes, both on a social and political level, and to contemplate how contemporary social institutions can adapt and evolve to address these challenges, as emphasized by Bowles in 2021 (p. 32). Scientific and technological solutions may give rise to conflicts among diverse societal interests and objectives, all of which play a role in shaping the development and implementation of innovations. These conflicts can manifest as social disputes stemming from various interpretations of the perceived threats to society. An analytical perspective from the realms of social and political philosophy and sociology can offer a valuable contribution in this context.

Big Data has seamlessly woven itself into the fabric of our lives, primarily through its capacity for real-time personalization across a myriad of services. It wields substantial influence over our choices, spanning from entertainment preferences such as the movies we watch and the music we listen to, to decisions concerning travel destinations, accommodations, social interactions, and even financial choices. Nevertheless, this pervasive technological integration has raised legitimate concerns about privacy, discrimination, and the presence of biases in these processes, as discussed by Bridle (pp. 142–143).

Some theorists argue that these developments embody a sort of technological determinism, emphasizing the idea that technology operates with a degree of autonomy. However, a more optimistic viewpoint suggests that responsible technology usage, ethical considerations, and education can empower individuals to effectively navigate this complex technological landscape, a perspective exemplified by Adam Greenfield in 2017 .

In this context, it is important to recognize the role of algorithms and new technologies in shaping our daily reality. Often, we use these technologies without understanding how they work or the algorithms behind them. As a result, our social reality becomes simplified, leading to a world of computational dominance. This raises questions about responsibility, ethics, awareness, and education in managing the impact of technology on society (Bridle, 2019 ).

In conclusion, the rapid advancement of technology, especially AI and big data, presents both opportunities and challenges for society. How we navigate these changes will depend on our ability to strike a balance between harnessing the potential benefits and addressing ethical, regulatory, and educational considerations. The impact of technology on our lives is profound, and it is essential to approach it with a nuanced understanding of its implications.

The goal of the text is to provide a comprehensive overview of the social impacts of the use of artificial intelligence (AI) and to lay the foundation for further discussions and research in this critical area. The text aims to address the compatibility of technological advances in AI with democratic values and social justice. It emphasizes the need for an interdisciplinary approach to studying these social impacts and advocates for collaboration among technical experts, ethicists, lawyers, and social scientists. In addition, the text underscores the importance of establishing appropriate regulations and ethical guidelines for AI use to create a society that benefits from technological progress while ensuring justice and protecting individual rights.

In formulating the article on the social impacts of artificial intelligence, the research methodology incorporated several key scientific methods, including a comprehensive literature review, and ethic and policy analysis. Firstly, the article extensively employed a literature review to establish a foundational understanding of the existing research landscape on the subject. By synthesizing findings from a wide range of academic sources, the authors ensured that their work was informed by the latest developments and perspectives in the field. In addition, the article integrated a thorough policy and legal analysis to assess the regulatory frameworks surrounding AI use. This involved scrutinizing existing policies and regulations, identifying potential gaps, and proposing recommendations for enhancing legal frameworks. The authors critically examined the ethical and legal implications of AI, contributing to the formulation of guidelines and regulations that align with democratic values and social justice. Together, these methods ensured a robust and multidimensional exploration of the social impacts of artificial intelligence, fostering a comprehensive understanding of the subject matter.

The critical methodology of this article is based on the Critical Theory of Technology and the philosophy of information, emphasizing digital transformation and the application of artificial intelligence. It focuses on interdisciplinary analysis, including sociology, anthropology, political science, and economics, to explore the social influences and structures affected by technological innovation. The approach combines philosophical and sociological theories to reveal the hidden mechanisms of datafication and commodification of digital data, while considering the ethical and political aspects of technological development. The analytical framework includes critical reflection of current digital and technological phenomena, examining user behavior, and assessing the social norms and values associated with technology.

Social risks of artificial intelligence

Examining the relationship between society and technology is a complex, interdisciplinary task that demands different perspectives and methodologies, including elements of sociology, anthropology, political science, economics, and other disciplines. Such transdisciplinary research includes the perspective of social influences, structures, and interactions, the analysis of the social consequences of technological innovation, the study of user behavior, and the examination of societal norms and values associated with technology. The study also explores the interaction of technology, culture, tradition, and social identity, including the economic consequences of technological innovation (the impact of technology on GDP growth, labor productivity, job creation, and competitiveness; analysis of investment in research and development, technology transfer, and technology trade).

On one hand, innovation and automation of production and services increase efficiency and productivity, which positively impacts GDP growth and job creation. On the other hand, the same process leads to changes in the employment structure, resulting in unintended negative consequences. (Gruetzemacher and Whittlestone, 2022 ) The political perspective involves examining the interaction of technology and the political system, decision-making processes, cyber security, internet regulation, the influence of technology giants on politics, and privacy and civil rights issues. With new technologies, individuals’ personal data is collected, processed, and used for profit, thereby threatening individual privacy and personal freedom. The possible distortion of public opinion and influence on elections increase the risk of political manipulation and the weakening of democratic processes (Zuboff and Schwandt, 2019 ).

The social risks associated with the use of artificial intelligence are primarily related to ethics, privacy, and social inequalities. AI algorithms can mirror and reinforce existing social prejudices and discrimination. If training data contains biases, AI algorithms can internalize and reproduce these biases, manifesting in various areas, including employment, crime, and finance. The use of AI involves the collection and analysis of vast amounts of data about individuals, potentially compromising their privacy and security. A lack of transparency in how AI algorithms operate can lead to mistrust and a sense of loss of control. At this level, the unintended consequence of AI usage, with significant security implications, includes the manipulation of public opinion, cyber attacks, or the development of autonomous weapons. Managing these social risks is crucial for the sustainable and ethical use of artificial intelligence, necessitating the creation of ethical guidelines and a transparent and responsible approach to AI. In the domain of the social impacts of technology, it is essential to recognize that discrimination perpetuated by AI algorithms can exacerbate existing inequalities (e.g., Noble, 2018 ). It must be expected that certain social groups will be negatively affected, particularly in areas like employment, housing, or crime. Digital inequality plays a significant role in this process. The issue of digital inequality encompasses disparities in access, use, or the ability to use modern information and communication technologies, affecting individuals, communities, or entire regions and countries. It is reflected in social and economic inequality, encompassing physical unavailability of technology, lack of access to relevant and quality content or services, limited digital competence in technology and internet use, safety rules, and the ability to search and verify information.

Furthermore, technological systems, including algorithms and artificial intelligence, can influence decision-making in social programs and assistance for the poor and marginalized populations. Some of these systems can exacerbate poverty by misidentifying the needs and social conditions of individuals. The digitization of public services and social programs can lead to social exclusion and the marginalization of those lacking access to modern technologies or lacking sufficient digital skills. This reinforces the importance of ethical oversight and transparency in the development and implementation of technological systems affecting social policy (Eubanks, 2018 ).

Current ethical, political, and social issues

If we focus on the ethical and political issues that arise in the context of new technologies, artificial intelligence, data collection, and algorithm application for users navigating the digital world, we can define the following problems.

With the increasing amount of personal data being collected and analyzed, there is concern about its misuse. Questions regarding privacy protection, regulation, and transparency are key issues that national states and international organizations must address. Cooperation with multinational corporations that collect personal data should be an integral part of this. (Chandler and Fuchs, 2019 ) Artificial intelligence algorithms may be burdened with bias, leading to unfair decisions and discrimination. This applies, for example, to decision-making in employment, credit scoring, and criminal justice. The values and ideologies of the technology designers are embedded in the algorithms. Avoiding these problems assumes a reflection of social reality itself. (Coeckelbergh, 2020 ) With the increasing use of AI in critical systems such as autonomous vehicles or medical devices, the importance of security also grows. There is concern about the potential misuse of AI for cyber attacks. Here, expert teams should play a role in preventing these threats, but the challenge lies in the constant development and growing threats.

Developers and organizations creating AI systems must address issues of ethics and responsibility. This includes deciding how systems will behave and how they will be used. It is assumed that a certain ethical concept will be embedded in the algorithms. However, the challenge is that technology must compete in the job market, so it tends to align with external market needs. A possible solution is the democratization of technologies. (Coeckelbergh, 2020 ) Policymakers and legal professionals are trying to adopt regulations and standards for artificial intelligence to ensure its safe and ethical use. However, it is a challenging task as AI technology is rapidly evolving. The implementation of new technologies cannot do without a philosophical framework, facing a big problem described in Moore’s Law: every eighteen months, the performance of computing circuits doubles, implying that technologies develop at an exponential rate. It means that technologies evolve faster than legal frameworks guaranteeing our safety. The potential of new technologies cannot be fully realized. The development of normative frameworks in the form of specific laws is logically slower than the development of technologies themselves. The development of artificial intelligence and automation can affect employment and the job market. Some professions may be threatened, while others may emerge. (Makridakis, 2017 , Zarifhonarvar, 2023 ) Use of Artificial Intelligence in the Military—Military use of AI brings complex ethical and security questions. There is concern about autonomous weapons and possible misuse of AI in military conflicts. (Ord, 2020 ) Accessibility and Equality of Access, Ownership of Data—The question of access and equality of access to AI technologies is also important. It is necessary to ensure that the benefits of AI are available to the widest possible range of people. The implementation of new technologies cannot do without a philosophical framework, facing a big problem described in Moore’s Law: every eighteen months, the performance of computing circuits doubles, implying that technologies develop at an exponential rate. It means that technologies evolve faster than legal frameworks guaranteeing our safety. The potential of new technologies cannot be fully realized. The development of normative frameworks in the form of specific laws is logically slower than the development of technologies themselves. (Allmer, 2017 , Ashok et al., 2022 ).

In general terms, we will draw on a methodological approach that was established relatively recently: the philosophy of technology (one of the first publications exploring this perspective was Simon, 1969 ; for more information see e.g. the overview in Berg-Olsen et al., 2009 ). This approach assesses the significance of technology and AI, its ethical dimensions and its other impacts on society; in many ways it draws on the earlier approach taken by the philosophy of science . Criticism of various social divisions associated with modern technologies is the domain of a subdiscipline known as ethics of technology , which became established as a fully fledged area of philosophy during the 20th century. One of the best-known forms of this subdiscipline (and one which besides the ethical dimension also drew on the perspectives of social and political philosophy) can be found in the first-generation Critical Theory of Society ; this approach will be at the core of our article—but in the current context of new communication and information technologies. (Feenberg, 2009 ).

The analysis of the digital transformation and implementation of AI

One of the leading representatives of the Critical Theory of Technology, Herbert Marcuse, noted that one problem of technology is that the expanding industrial base and the conditions imposed by the social order of technocracy are suppressing human individuality in favor of standardized efficiency. A similar approach has been taken towards the emergence of a new modern rationality, which has accompanied the development of technologies during the era of industrialization and which represents the basis of mass production as well as impacting upon other social relationships (Adorno and Horkheimer, 2007 ). The second generation of Critical Theory draws on these analyses, e.g. Habermas’s rejection of technological neutrality (Feenberg 2014 ). This approach rests on the assumption that economic and social growth is determined by scientific and technological progress, which in the final instance is a political problem, because political problems are reduced to technical problems and their solution is delegated to experts rather than politicians.

A new type of technology and AI has arrived, which is difficult to cognitively reflect with. One can no longer perceive what the new technologies are capable of—we perceive them as black boxes. The whole/totality of these technologies creates a new kind of sublime that we are unable to interpret (like in Kant and Lyotard’s theory of art). This has the effect of transforming the social subject itself. However, this phenomenon cannot be interpreted by technological determinism; we need to extend the Critical Theory of Technology with a new framework that will offer a certain cognitive map in this infinite diversity. It will therefore be based on Fredric Jameson’s theory that will be combined with the Critical Theory of Technology, philosophy of technology and sociology (Jameson, 1992 ). Of course, the main goal will be to trace the contradictions between the individual, technology and society.

The representatives of the contemporary Critical Theory of technology have pointed out that the current digital world is witnessing a similar process of alienation to that which was previously identified using the methodology of classical critical theory . For example, the theory developed by Allmer ( 2017 ) uses tools enabling shared data to be subjected to critical investigation from the perspective of economic and power relations. Although it appears that data are handled by users, in reality these data are owned by large corporations. Allmer claims that user data are exploited for the accumulation of capital—which, in this digital environment, creates social divisions (Allmer, 2017 , p. 21). In his view, the fact that this principle of capital accumulation has migrated from the material environment of physical commodities into the digital world reflects the gradual evolution of commodification.

However, the commodification of public goods (such as data) brings with it numerous complications: for example, the practice of digital reproduction accentuates the privatization of data (see e.g. Rakowski and Kowalikova, pp. 29–30, 2020 ).

Our use of the critical theory of technology means that we will analyze contemporary societal phenomena using a dialectical method with respect to society as a whole—yet for the purposes of our analyse, in order to understand contemporary relations within society, it is necessary to proceed via an analysis of technologies. It is therefore essential to select a mediating framework positioned between technology and society. The Critical Theory of Technology approaches such analyses using an interpretative framework according to which the asymmetry of power relations is incorporated into the actual design of technologies. Technology is understood as a reflection of societal relations, and for this reason it cannot be viewed in neutral terms (this is the fundamental paradox of empirical analysis). From this perspective, technology cannot be designed outside the societal context. The goals of technology thus correspond with the goals of its own production process (Rakowski and Kowalikova, pp. 30, 2020 ).

In this way, the Critical Theory of Technology draws attention to the socially conditioned construction of technology and the impacts of technology on society. Critical theory explores the dialectic of substance and phenomenon, as well as focusing on societal reality which manifests the specific historical activities of humans (Allmer, 2017 , p. 25). However, a problem arises if we ask what precisely should be considered a phenomenon, and how structures should be interpreted in the context of digital capitalism (see also Rakowski and Kowalikova, p. 30, 2020 ). How should we interpret the position of an individual in the context of technologies, power relations of technologies, mediation between humans and technologies, or the ideology of technologies? These are among the fundamental interpretative questions explored in this study.

Although Critical Theory is essentially value-burdened, in our opinion it should conduct its analysis in a neutral manner: technology should neither be adored nor demonized; we need to be able to identify both good and bad aspects of technology, and only in this way will we have the tools to transform it, i.e., to democratize its latent functions. This approach represents our methodological innovation. It means that we want to use the critical theory of technology for the purpose of analysis and description—not to exploit its value-burdened nature in normative criticism (Rakowski and Kowalikova, p. 31, 2020 ).

Following Andrew Feenberg ( 2009 ), we can distinguish two main approaches in the theory of technology. The first is instrumental : technical tools are viewed as neutral resources that only serve societal goals, helping to achieve efficiency. This approach is purely functional; technology is detached from the context of political ideology. The second approach is substantive : it denies that technology is neutral, and accentuates its negative impact on humanity and nature. According to Allmer ( 2017 ), a third approach—critical and dialectical—is needed. This approach constitutes an interpretative framework according to which technology cannot be separated from its use: technology is already defined before it comes into existence, and it emerges into a specific value context, thus contributing to the maintenance of existing social relationships (Allmer, 2017 , p. 38). In this study we draw on Allmer’s approach, but our methodology—in line with Feenberg—takes a non-deterministic approach to technology. We do not view technology merely as a set of devices or a sum of rational goals; in our approach, the nature of technology is also affected by factors such as public opinion—i.e., a normative requirement of democratic instrumentalization (see Feenberg, 2009 , p. 146). We thus view technology in connection with specific social discourses—experts, norms, institutions (Rakowski and Kowalikova, pp. 31–32, 2020 ).

In our view, this approach needs to be further elaborated. It frequently happens that technology becomes imbued (whether consciously or unconsciously) with specific values, and a hermeneutics of technology should be capable of interpreting these values. Technologies contribute to the formation of the principles according to which we live—yet on the other hand, technologies can, to a certain extent, represent either our own values or the values of others. Although there exists a tendency to view technology and politics as separate domains, in our opinion technology is not a neutral resource: on the one hand it has its own value (and it can reflect various private intentions), but on the other hand its course of development can be determined by society. Applying this methodological approach, this study thus views technology as an outcome of numerous factors: the meaning of technology is only defined once it is used in the societal context.

So, the methodology will primarily draw on three types of methodologies and methodological approaches:

analysis of the political dimension of technologies and interpretation of social relations applying the critical theory of technology (see Andrew Feenberg, Cristian Fuchs or Thomas Allmer);

application of the philosophy of information to questions of knowledge/gnoseology of the social universe as a Big Data project (as elaborated in the work of the Italian philosopher Luciano Floridi), using analytical tools developed by us to explore contemporary information and communication technologies;

we will also focus on the general concept of the datification of knowledge, which expresses the current trend in which knowledge is converted into digital form and subsequently analyzed. This advantageously benefits those who have access to algorithms and artificial intelligence capable of analyzing this vast amount of data. A didactic tool in this context can be so-called computational thinking.

These three distinct traditions need to be integrated, because analysis of contemporary phenomena such as Big Data is lacking in the first approach. On the other hand, Luciano Floridi’s theory of information ( 2014 ) does not incorporate a political analysis of (new) technologies which would offer more than a mere description of new forms of the postmodern, information-filled world. Such an analysis would not allow us to take a critical view of the contemporary digital world and to explore the negative aspects of the transformation of data into capital, the instrumentalization of data, innovations in business models, and similar issues. One innovative aspect of our methodology is that our analysis will encompass the political context of technologies while also taking into consideration how technologies are transforming social subjects.

Challenges in the analyses

This innovative methodological approach will be one of the main contributions to the contemporary debate:

to identify the most important elements in the earlier and contemporary critical theory of technology which are appropriate for the analysis to be conducted, to elaborate and apply our own interpretative method integrating various theories and methodological concepts from the field of digital technologies with the topic of digitalization;

to analyze the divisions arising from the use of selected new technologies and AI;

to reflect on the construction of contemporary reality as influenced by selected modern technologies and AI;

to delineate the roles played by these selected technologies and AI in society;

to investigate the social context of the risks associated with selected modern technologies, their mutual relationships and complexity derived from using AI.

Power, politics, and data

These problems are complex and require collaboration between technology companies, governments, academic institutions, and society as a whole. It is likely that we will continue to grapple with these issues for a long time, and it will be necessary to seek permanent solutions. In our reflection, we can see how social disparities, similar to classical contradictions in the material world, are now appearing in the digital realm. It is necessary to critically examine shared data from the perspective of economic-power relations. Although users seem to handle data, they are actually owned by someone else who truly decides how to deal with them. This is certainly disconcerting, and it is necessary to explore its impact on users.

Through user data, capital accumulates easily, turning this digital environment into an arena of struggles where class and social disparities emerge. The transfer of the principle of capital accumulation from the material environment of commodities to the digital world is part of the evolution of commodification. However, commodifying public goods (such as data) brings a host of complications, including the politicization of privacy protection. Therefore, it is necessary to create new forms of capital, ideally involving the user who constantly produces data in this digital production. Following the vocabulary of Critical Theory , this phenomenon can be labeled as digital ideology and digital exploitation.

Numerous present-day conflicts between individuals and the online environment arise from the reconfiguration of the way data is interpreted. It is clear that a similar contradiction to that seen in the physical world emerges here, wherein an unfair market position arises based on knowledge, as well as access to interpreted data—algorithms and artificial intelligence provide advantages.

Several challenges stem from the way technology influences and shapes our perspective of the world, often subtly and not immediately obvious to those experiencing it. In a setting where algorithms are not transparent, knowledge is transformed into data, and imbalances exist in technology’s creation and design, it becomes imperative to explore how individuals perceive and understand their surroundings.

This highlights the challenges and complexities that arise in our relationship with technology, especially when it comes to how technology influences our perception and understanding of the world. (Bridle J, 2019 ) We can identify several problems.

Technology plays a significant role in shaping our experiences and interactions with the world around us. It influences how we perceive, understand, and engage with our surroundings. Technology acts as a filter, influencing how we access and process information. This filtering can be subtle and may not always be apparent to individuals. Technology can prioritize certain information while obscuring or diminishing others, shaping our perspectives and knowledge base. The inner workings of algorithms and data-driven systems are often opaque and lack transparency. This lack of transparency makes it difficult to understand how technology makes decisions or shapes our experiences. The opacity of algorithms can lead to biases and unintended consequences. Traditional knowledge is increasingly being transformed into digital data, enabling it to be processed, analyzed, and manipulated by technology. This datafication of knowledge has both positive and negative implications. It can facilitate access to information and enable new forms of analysis, but it can also lead to the loss of contextual nuances and the prioritization of quantifiable data over qualitative insights. Technology is often designed and produced by specific groups or organizations, leading to power imbalances and biases in how technology operates and what it prioritizes. The designers and producers of technology have significant influence over how it shapes our experiences and understandings. Given the complexities introduced by technology, it is crucial to examine how individuals’ understanding of the world is affected. The epistemic position of the subject, in terms of their knowledge and understanding, is shaped by the technological landscape they inhabit. Understanding the impact of technology on individual epistemologies is essential for navigating the increasingly tech-mediated world.

In summary, points underscore the need for further examination of how technology influences our knowledge and perception of the world, especially in the context of opaque algorithms, data-driven knowledge, and disparities in technology design and production. It highlights the importance of being aware of these influences and their potential consequences.

If we decide to bypass the Critical Theory of Technology and attempt to find a tool through which the subject of knowledge could defend itself, we should take an educational approach and contrast the “datafication of knowledge” with the term “computational thinking.”

Computational Thinking is currently a vital skill for navigating the ever-changing landscape of technology, applications, and the vast realm of big data. Educational institutions, from primary to secondary schools in modern countries, recognize the significance of Computational Thinking. It provides students with the adaptability and the ability to view the natural world as a series of logical operations that can be programmed through software. It encompasses attitudes and skills that empower individuals to identify and tackle complex problems, fostering a mindset of flexibility. Graduates equipped with Computational Thinking possess versatile thinking, making them more competitive in the labor market, which is gradually being shaped by automation and robotization, known as Industry 4.0. Graduates no longer perceive technology as a mysterious black box they merely use; instead, they actively engage with it by interpreting, utilizing, and modifying it. (Rakowski et al., 2023 ).

Computational Thinking acknowledges the computational aspects of the natural and technological environment that surrounds us. It enables adjustments in a rapidly evolving world, bringing about significant innovations to both individuals and society as a whole. It offers a set of problem-solving approaches aimed at making computers solve specific tasks. Within the realm of technological innovation, this is considered a fundamental skill necessary for meeting the increasing demands of the fourth industrial revolution. These abilities encompass a range of cognitive faculties that transform intricate real-world problems into solvable forms that can be handled by a machine without additional human intervention.

To design algorithms or programs capable of performing computations and to comprehend the underlying processes of natural information, a distinct form of thinking is essential. Computational Thinking encompasses various modes of thinking and problem-solving skills that can be honed through practice and teamwork. It represents a rich set of interdisciplinary abilities applicable to a wide array of subjects in both the natural and social sciences. It does not reflect the way computers think, even though we can program them to mimic this approach; instead, it comprises various human problem-solving abilities resulting from the study of computation’s nature. Computational Thinking draws on skills such as creativity, interpretation, and abstraction, coupled with the capacity to think mathematically, logically, and algorithmically, scrutinizing details while inventing novel methods to enhance processes. Computational Thinking harmonizes these diverse modes of thinking, serving as a dependable tool for designing algorithms (Rakowski et al., 2023 ).

The study has identified that the use of artificial intelligence carries a variety of political and social impacts, influencing both human and online environments, while also transferring societal contradictions from the material world to the digital realm. These impacts include, for example, changes in political power through political culture, which can strengthen or weaken the positions of governments, businesses, civil society, and individuals. Furthermore, there is a shift in social structure, as digital technology alters how people communicate with society. In addition, social values are changing as digital technologies influence the perception and evaluation of the world around us. It is also evident that a digital class is emerging, producing data but lacking access to these data.

The study has also revealed that these changes can lead to political and social conflicts. These conflicts include tensions between democratic values and data collection, where digital technologies can jeopardize the privacy and freedom of individuals. Other conflicts arise between market economy and data sharing, where gathering information about people can lead to discrimination, ethical dilemmas, and cognitive biases. There are also conflicts between individual rights and public well-being, as monitoring and influencing individuals’ behavior may disrupt their freedom.

In response to these conflicts, the study recommends the implementation of political and social measures aimed at strengthening democratic values and protecting human rights. This includes better regulation of digital technologies, supporting civil society in advocating for democratic values online, and educating the public about the political and social consequences of digital transformation. As shown, there is a need to democratize technology. On one side is ethics, embedded in algorithms and artificial intelligence, and on the other side are civil society initiatives that must exert pressure on norms.

The study also proposes three areas for further research. The first concerns the impact of digital transformation on various social groups, such as minorities, women, and people from economically disadvantaged areas. The second area involves exploring the political and social mechanisms leading to conflicts in human and online environments. The last area focuses on finding new solutions to political and social conflicts in both of these environments.

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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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All authors contributed to the paper conception, methodology and formal analysis and investigation. The first draft of the manuscript was written by RR, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conceptualization: RR and PK; Methodology: RR and PK; Writing—original draft: RR; Writing—review and editing: RR and PK; Formal analysis and investigation: RR and PK.

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Rakowski, R., Kowaliková, P. The political and social contradictions of the human and online environment in the context of artificial intelligence applications. Humanit Soc Sci Commun 11 , 289 (2024). https://doi.org/10.1057/s41599-024-02725-y

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Received : 15 November 2023

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DOI : https://doi.org/10.1057/s41599-024-02725-y

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