negative effects of artificial intelligence essay

May 25, 2023

Here’s Why AI May Be Extremely Dangerous—Whether It’s Conscious or Not

Artificial intelligence algorithms will soon reach a point of rapid self-improvement that threatens our ability to control them and poses great potential risk to humanity

By Tamlyn Hunt

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“The idea that this stuff could actually get smarter than people.... I thought it was way off…. Obviously, I no longer think that,” Geoffrey Hinton, one of Google's top artificial intelligence scientists, also known as “ the godfather of AI ,” said after he quit his job in April so that he can warn about the dangers of this technology .

He’s not the only one worried. A 2023 survey of AI experts found that 36 percent fear that AI development may result in a “nuclear-level catastrophe.” Almost 28,000 people have signed on to an open letter written by the Future of Life Institute, including Steve Wozniak, Elon Musk, the CEOs of several AI companies and many other prominent technologists, asking for a six-month pause or a moratorium on new advanced AI development.

As a researcher in consciousness, I share these strong concerns about the rapid development of AI, and I am a co-signer of the Future of Life open letter.

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Why are we all so concerned? In short: AI development is going way too fast.

The key issue is the profoundly rapid improvement in conversing among the new crop of advanced "chatbots," or what are technically called “large language models” (LLMs). With this coming “AI explosion,” we will probably have just one chance to get this right.

If we get it wrong, we may not live to tell the tale. This is not hyperbole.

This rapid acceleration promises to soon result in “artificial general intelligence” (AGI), and when that happens, AI will be able to improve itself with no human intervention. It will do this in the same way that, for example, Google’s AlphaZero AI learned how to play chess better than even the very best human or other AI chess players in just nine hours from when it was first turned on. It achieved this feat by playing itself millions of times over.

A team of Microsoft researchers analyzing OpenAI’s GPT-4 , which I think is the best of the new advanced chatbots currently available, said it had, "sparks of advanced general intelligence" in a new preprint paper .

In testing GPT-4, it performed better than 90 percent of human test takers on the Uniform Bar Exam, a standardized test used to certify lawyers for practice in many states. That figure was up from just 10 percent in the previous GPT-3.5 version, which was trained on a smaller data set. They found similar improvements in dozens of other standardized tests.

Most of these tests are tests of reasoning. This is the main reason why Bubeck and his team concluded that GPT-4 “could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.”

This pace of change is why Hinton told the New York Times : "Look at how it was five years ago and how it is now. Take the difference and propagate it forwards. That’s scary.” In a mid-May Senate hearing on the potential of AI, Sam Altman, the head of OpenAI called regulation “crucial.”

Once AI can improve itself, which may be not more than a few years away, and could in fact already be here now, we have no way of knowing what the AI will do or how we can control it. This is because superintelligent AI (which by definition can surpass humans in a broad range of activities) will—and this is what I worry about the most—be able to run circles around programmers and any other human by manipulating humans to do its will; it will also have the capacity to act in the virtual world through its electronic connections, and to act in the physical world through robot bodies.

This is known as the “control problem” or the “alignment problem” (see philosopher Nick Bostrom’s book Superintelligence for a good overview ) and has been studied and argued about by philosophers and scientists, such as Bostrom, Seth Baum and Eliezer Yudkowsky , for decades now.

I think of it this way: Why would we expect a newborn baby to beat a grandmaster in chess? We wouldn’t. Similarly, why would we expect to be able to control superintelligent AI systems? (No, we won’t be able to simply hit the off switch, because superintelligent AI will have thought of every possible way that we might do that and taken actions to prevent being shut off.)

Here’s another way of looking at it: a superintelligent AI will be able to do in about one second what it would take a team of 100 human software engineers a year or more to complete. Or pick any task, like designing a new advanced airplane or weapon system, and superintelligent AI could do this in about a second.

Once AI systems are built into robots, they will be able to act in the real world, rather than only the virtual (electronic) world, with the same degree of superintelligence, and will of course be able to replicate and improve themselves at a superhuman pace.

Any defenses or protections we attempt to build into these AI “gods,” on their way toward godhood, will be anticipated and neutralized with ease by the AI once it reaches superintelligence status. This is what it means to be superintelligent.

We won’t be able to control them because anything we think of, they will have already thought of, a million times faster than us. Any defenses we’ve built in will be undone, like Gulliver throwing off the tiny strands the Lilliputians used to try and restrain him.

Some argue that these LLMs are just automation machines with zero consciousness , the implication being that if they’re not conscious they have less chance of breaking free from their programming. Even if these language models, now or in the future, aren’t at all conscious, this doesn’t matter. For the record, I agree that it’s unlikely that they have any actual consciousness at this juncture—though I remain open to new facts as they come in.

Regardless, a nuclear bomb can kill millions without any consciousness whatsoever. In the same way, AI could kill millions with zero consciousness, in a myriad ways, including potentially use of nuclear bombs either directly (much less likely) or through manipulated human intermediaries (more likely).

So, the debates about consciousness and AI really don’t figure very much into the debates about AI safety.

Yes, language models based on GPT-4 and many other models are already circulating widely . But the moratorium being called for is to stop development of any new models more powerful than 4.0—and this can be enforced, with force if required. Training these more powerful models requires massive server farms and energy. They can be shut down.

My ethical compass tells me that it is very unwise to create these systems when we know already we won’t be able to control them, even in the relatively near future. Discernment is knowing when to pull back from the edge. Now is that time.

We should not open Pandora’s box any more than it already has been opened.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of  Scientific American.

12 Risks and Dangers of Artificial Intelligence (AI)

As AI grows more sophisticated and widespread, the voices warning against the potential dangers of artificial intelligence grow louder.

“These things could get more intelligent than us and could decide to take over, and we need to worry now about how we prevent that happening,” said Geoffrey Hinton , known as the “Godfather of AI” for his foundational work on machine learning and neural network algorithms. In 2023, Hinton left his position at Google so that he could “ talk about the dangers of AI ,” noting a part of him even regrets his life’s work .

The renowned computer scientist isn’t alone in his concerns.

Tesla and SpaceX founder Elon Musk, along with over 1,000 other tech leaders, urged in a 2023 open letter to put a pause on large AI experiments, citing that the technology can “pose profound risks to society and humanity.”

Dangers of Artificial Intelligence

• Automation-spurred job loss
• Privacy violations
• Algorithmic bias caused by bad data
• Socioeconomic inequality
• Market volatility
• Weapons automatization
• Uncontrollable self-aware AI

Whether it’s the increasing automation of certain jobs , gender and racially biased algorithms or autonomous weapons that operate without human oversight (to name just a few), unease abounds on a number of fronts. And we’re still in the very early stages of what AI is really capable of.

12 Dangers of AI

Questions about who’s developing AI and for what purposes make it all the more essential to understand its potential downsides. Below we take a closer look at the possible dangers of artificial intelligence and explore how to manage its risks.

Is AI Dangerous?

The tech community has long debated the threats posed by artificial intelligence. Automation of jobs, the spread of fake news and a dangerous arms race of AI-powered weaponry have been mentioned as some of the biggest dangers posed by AI.

1. Lack of AI Transparency and Explainability 

AI and deep learning models can be difficult to understand, even for those that work directly with the technology . This leads to a lack of transparency for how and why AI comes to its conclusions, creating a lack of explanation for what data AI algorithms use, or why they may make biased or unsafe decisions. These concerns have given rise to the use of explainable AI , but there’s still a long way before transparent AI systems become common practice.

2. Job Losses Due to AI Automation

AI-powered job automation is a pressing concern as the technology is adopted in industries like marketing , manufacturing and healthcare . By 2030, tasks that account for up to 30 percent of hours currently being worked in the U.S. economy could be automated — with Black and Hispanic employees left especially vulnerable to the change — according to McKinsey . Goldman Sachs even states 300 million full-time jobs could be lost to AI automation.

“The reason we have a low unemployment rate, which doesn’t actually capture people that aren’t looking for work, is largely that lower-wage service sector jobs have been pretty robustly created by this economy,” futurist Martin Ford told Built In. With AI on the rise, though, “I don’t think that’s going to continue.”

As AI robots become smarter and more dexterous, the same tasks will require fewer humans. And while AI is estimated to create 97 million new jobs by 2025 , many employees won’t have the skills needed for these technical roles and could get left behind if companies don’t upskill their workforces .

“If you’re flipping burgers at McDonald’s and more automation comes in, is one of these new jobs going to be a good match for you?” Ford said. “Or is it likely that the new job requires lots of education or training or maybe even intrinsic talents — really strong interpersonal skills or creativity — that you might not have? Because those are the things that, at least so far, computers are not very good at.”

Even professions that require graduate degrees and additional post-college training aren’t immune to AI displacement.

As technology strategist Chris Messina has pointed out, fields like law and accounting are primed for an AI takeover. In fact, Messina said, some of them may well be decimated. AI already is having a significant impact on medicine. Law and accounting are next, Messina said, the former being poised for “a massive shakeup.”

“Think about the complexity of contracts, and really diving in and understanding what it takes to create a perfect deal structure,” he said in regards to the legal field. “It’s a lot of attorneys reading through a lot of information — hundreds or thousands of pages of data and documents. It’s really easy to miss things. So AI that has the ability to comb through and comprehensively deliver the best possible contract for the outcome you’re trying to achieve is probably going to replace a lot of corporate attorneys.”

More on Artificial Intelligence AI Copywriting: Why Writing Jobs Are Safe

3. Social Manipulation Through AI Algorithms

Social manipulation also stands as a danger of artificial intelligence. This fear has become a reality as politicians rely on platforms to promote their viewpoints, with one example being Ferdinand Marcos, Jr., wielding a TikTok troll army to capture the votes of younger Filipinos during the Philippines’ 2022 election. 

TikTok, which is just one example of a social media platform that relies on AI algorithms , fills a user’s feed with content related to previous media they’ve viewed on the platform. Criticism of the app targets this process and the algorithm’s failure to filter out harmful and inaccurate content, raising concerns over TikTok’s ability to protect its users from misleading information. 

Online media and news have become even murkier in light of AI-generated images and videos, AI voice changers as well as deepfakes infiltrating political and social spheres. These technologies make it easy to create realistic photos, videos, audio clips or replace the image of one figure with another in an existing picture or video. As a result, bad actors have another avenue for sharing misinformation and war propaganda , creating a nightmare scenario where it can be nearly impossible to distinguish between creditable and faulty news. 

“No one knows what’s real and what’s not,” Ford said. “So it really leads to a situation where you literally cannot believe your own eyes and ears; you can’t rely on what, historically, we’ve considered to be the best possible evidence... That’s going to be a huge issue.”

More on Artificial Intelligence How to Spot Deepfake Technology

4. Social Surveillance With AI Technology

In addition to its more existential threat, Ford is focused on the way AI will adversely affect privacy and security. A prime example is China’s use of facial recognition technology in offices, schools and other venues. Besides tracking a person’s movements, the Chinese government may be able to gather enough data to monitor a person’s activities, relationships and political views. 

Another example is U.S. police departments embracing predictive policing algorithms to anticipate where crimes will occur. The problem is that these algorithms are influenced by arrest rates, which disproportionately impact Black communities . Police departments then double down on these communities, leading to over-policing and questions over whether self-proclaimed democracies can resist turning AI into an authoritarian weapon.

“Authoritarian regimes use or are going to use it,” Ford said. “The question is, How much does it invade Western countries, democracies, and what constraints do we put on it?”

Related Are Police Robots the Future of Law Enforcement?

5. Lack of Data Privacy Using AI Tools

If you’ve played around with an AI chatbot or tried out an AI face filter online, your data is being collected — but where is it going and how is it being used? AI systems often collect personal data to customize user experiences or to help train the AI models you’re using (especially if the AI tool is free). Data may not even be considered secure from other users when given to an AI system, as one bug incident that occurred with ChatGPT in 2023 “ allowed some users to see titles from another active user’s chat history .” While there are laws present to protect personal information in some cases in the United States, there is no explicit federal law that protects citizens from data privacy harm experienced by AI. 

6. Biases Due to AI

Various forms of AI bias are detrimental too. Speaking to the New York Times , Princeton computer science professor Olga Russakovsky said AI bias goes well beyond gender and race . In addition to data and algorithmic bias (the latter of which can “amplify” the former), AI is developed by humans — and humans are inherently biased .

“A.I. researchers are primarily people who are male, who come from certain racial demographics, who grew up in high socioeconomic areas, primarily people without disabilities,” Russakovsky said. “We’re a fairly homogeneous population, so it’s a challenge to think broadly about world issues.”

The limited experiences of AI creators may explain why speech-recognition AI often fails to understand certain dialects and accents, or why companies fail to consider the consequences of a chatbot impersonating notorious figures in human history. Developers and businesses should exercise greater care to avoid recreating powerful biases and prejudices that put minority populations at risk.  

7. Socioeconomic Inequality as a Result of AI 

If companies refuse to acknowledge the inherent biases baked into AI algorithms, they may compromise their DEI initiatives through AI-powered recruiting . The idea that AI can measure the traits of a candidate through facial and voice analyses is still tainted by racial biases, reproducing the same discriminatory hiring practices businesses claim to be eliminating.  

Widening socioeconomic inequality sparked by AI-driven job loss is another cause for concern, revealing the class biases of how AI is applied. Workers who perform more manual, repetitive tasks have experienced wage declines as high as 70 percent because of automation, with office and desk workers remaining largely untouched in AI’s early stages. However, the increase in generative AI use is already affecting office jobs , making for a wide range of roles that may be more vulnerable to wage or job loss than others.

Sweeping claims that AI has somehow overcome social boundaries or created more jobs fail to paint a complete picture of its effects. It’s crucial to account for differences based on race, class and other categories. Otherwise, discerning how AI and automation benefit certain individuals and groups at the expense of others becomes more difficult.

8. Weakening Ethics and Goodwill Because of AI

Along with technologists, journalists and political figures, even religious leaders are sounding the alarm on AI’s potential pitfalls. In a 2023 Vatican meeting and in his message for the 2024 World Day of Peace , Pope Francis called for nations to create and adopt a binding international treaty that regulates the development and use of AI.

Pope Francis warned against AI’s ability to be misused, and “create statements that at first glance appear plausible but are unfounded or betray biases.” He stressed how this could bolster campaigns of disinformation, distrust in communications media, interference in elections and more — ultimately increasing the risk of “fueling conflicts and hindering peace.” 

The rapid rise of generative AI tools gives these concerns more substance. Many users have applied the technology to get out of writing assignments, threatening academic integrity and creativity. Plus, biased AI could be used to determine whether an individual is suitable for a job, mortgage, social assistance or political asylum, producing possible injustices and discrimination, noted Pope Francis. 

“The unique human capacity for moral judgment and ethical decision-making is more than a complex collection of algorithms,” he said. “And that capacity cannot be reduced to programming a machine.”

More on Artificial Intelligence What Are AI Ethics?

9. Autonomous Weapons Powered By AI

As is too often the case, technological advancements have been harnessed for the purpose of warfare. When it comes to AI, some are keen to do something about it before it’s too late: In a 2016 open letter , over 30,000 individuals, including AI and robotics researchers, pushed back against the investment in AI-fueled autonomous weapons. 

“The key question for humanity today is whether to start a global AI arms race or to prevent it from starting,” they wrote. “If any major military power pushes ahead with AI weapon development, a global arms race is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow.”

This prediction has come to fruition in the form of Lethal Autonomous Weapon Systems , which locate and destroy targets on their own while abiding by few regulations. Because of the proliferation of potent and complex weapons, some of the world’s most powerful nations have given in to anxieties and contributed to a tech cold war .  

Many of these new weapons pose major risks to civilians on the ground, but the danger becomes amplified when autonomous weapons fall into the wrong hands. Hackers have mastered various types of cyber attacks , so it’s not hard to imagine a malicious actor infiltrating autonomous weapons and instigating absolute armageddon.  

If political rivalries and warmongering tendencies are not kept in check, artificial intelligence could end up being applied with the worst intentions. Some fear that, no matter how many powerful figures point out the dangers of artificial intelligence, we’re going to keep pushing the envelope with it if there’s money to be made.   

“The mentality is, ‘If we can do it, we should try it; let’s see what happens,” Messina said. “‘And if we can make money off it, we’ll do a whole bunch of it.’ But that’s not unique to technology. That’s been happening forever.’”

10. Financial Crises Brought About By AI Algorithms

The financial industry has become more receptive to AI technology’s involvement in everyday finance and trading processes. As a result, algorithmic trading could be responsible for our next major financial crisis in the markets.

While AI algorithms aren’t clouded by human judgment or emotions, they also don’t take into account contexts , the interconnectedness of markets and factors like human trust and fear. These algorithms then make thousands of trades at a blistering pace with the goal of selling a few seconds later for small profits. Selling off thousands of trades could scare investors into doing the same thing, leading to sudden crashes and extreme market volatility.

Instances like the 2010 Flash Crash and the Knight Capital Flash Crash serve as reminders of what could happen when trade-happy algorithms go berserk, regardless of whether rapid and massive trading is intentional.  

This isn’t to say that AI has nothing to offer to the finance world. In fact, AI algorithms can help investors make smarter and more informed decisions on the market. But finance organizations need to make sure they understand their AI algorithms and how those algorithms make decisions. Companies should consider whether AI raises or lowers their confidence before introducing the technology to avoid stoking fears among investors and creating financial chaos.

11. Loss of Human Influence

An overreliance on AI technology could result in the loss of human influence — and a lack in human functioning — in some parts of society. Using AI in healthcare could result in reduced human empathy and reasoning , for instance. And applying generative AI for creative endeavors could diminish human creativity and emotional expression . Interacting with AI systems too much could even cause reduced peer communication and social skills. So while AI can be very helpful for automating daily tasks, some question if it might hold back overall human intelligence, abilities and need for community.

12. Uncontrollable Self-Aware AI

There also comes a worry that AI will progress in intelligence so rapidly that it will become sentient , and act beyond humans’ control — possibly in a malicious manner. Alleged reports of this sentience have already been occurring, with one popular account being from a former Google engineer who stated the AI chatbot LaMDA was sentient and speaking to him just as a person would. As AI’s next big milestones involve making systems with artificial general intelligence , and eventually artificial superintelligence , cries to completely stop these developments continue to rise .

More on Artificial Intelligence What Is the Eliza Effect?

How to Mitigate the Risks of AI

AI still has numerous benefits , like organizing health data and powering self-driving cars. To get the most out of this promising technology, though, some argue that plenty of regulation is necessary.

“There’s a serious danger that we’ll get [AI systems] smarter than us fairly soon and that these things might get bad motives and take control,” Hinton told NPR . “This isn’t just a science fiction problem. This is a serious problem that’s probably going to arrive fairly soon, and politicians need to be thinking about what to do about it now.”

Develop Legal Regulations

AI regulation has been a main focus for dozens of countries , and now the U.S. and European Union are creating more clear-cut measures to manage the rising sophistication of artificial intelligence. In fact, the White House Office of Science and Technology Policy (OSTP) published the AI Bill of Rights in 2022, a document outlining to help responsibly guide AI use and development. Additionally, President Joe Biden issued an executive order in 2023 requiring federal agencies to develop new rules and guidelines for AI safety and security.

Although legal regulations mean certain AI technologies could eventually be banned, it doesn’t prevent societies from exploring the field.  

Ford argues that AI is essential for countries looking to innovate and keep up with the rest of the world.

“You regulate the way AI is used, but you don’t hold back progress in basic technology. I think that would be wrong-headed and potentially dangerous,” Ford said. “We decide where we want AI and where we don’t; where it’s acceptable and where it’s not. And different countries are going to make different choices.”

More on Artificial Intelligence Will This Election Year Be a Turning Point for AI Regulation?

Establish Organizational AI Standards and Discussions

On a company level, there are many steps businesses can take when integrating AI into their operations. Organizations can develop processes for monitoring algorithms, compiling high-quality data and explaining the findings of AI algorithms. Leaders could even make AI a part of their company culture and routine business discussions, establishing standards to determine acceptable AI technologies.

Guide Tech With Humanities Perspectives

Though when it comes to society as a whole, there should be a greater push for tech to embrace the diverse perspectives of the humanities . Stanford University AI researchers Fei-Fei Li and John Etchemendy make this argument in a 2019 blog post that calls for national and global leadership in regulating artificial intelligence:   

“The creators of AI must seek the insights, experiences and concerns of people across ethnicities, genders, cultures and socio-economic groups, as well as those from other fields, such as economics, law, medicine, philosophy, history, sociology, communications, human-computer-interaction, psychology, and Science and Technology Studies (STS).”

Balancing high-tech innovation with human-centered thinking is an ideal method for producing responsible AI technology and ensuring the future of AI remains hopeful for the next generation. The dangers of artificial intelligence should always be a topic of discussion, so leaders can figure out ways to wield the technology for noble purposes. 

“I think we can talk about all these risks, and they’re very real,” Ford said. “But AI is also going to be the most important tool in our toolbox for solving the biggest challenges we face.”

Frequently Asked Questions

What is ai.

AI (artificial intelligence) describes a machine's ability to perform tasks and mimic intelligence at a similar level as humans.

Is AI dangerous?

AI has the potential to be dangerous, but these dangers may be mitigated by implementing legal regulations and by guiding AI development with human-centered thinking.

Can AI cause human extinction?

If AI algorithms are biased or used in a malicious manner — such as in the form of deliberate disinformation campaigns or autonomous lethal weapons — they could cause significant harm toward humans. Though as of right now, it is unknown whether AI is capable of causing human extinction.

What happens if AI becomes self-aware?

Self-aware AI has yet to be created, so it is not fully known what will happen if or when this development occurs.

Some suggest self-aware AI may become a helpful counterpart to humans in everyday living, while others suggest that it may act beyond human control and purposely harm humans.

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Opinion Guest Essay

The True Threat of Artificial Intelligence

Credit... Mathieu Larone

Supported by

By Evgeny Morozov

Mr. Morozov is the author of “To Save Everything, Click Here: The Folly of Technological Solutionism” and the host of the forthcoming podcast “ The Santiago Boys .”

• June 30, 2023

In May, more than 350 technology executives, researchers and academics signed a statement warning of the existential dangers of artificial intelligence. “Mitigating the risk of extinction from A.I. should be a global priority alongside other societal-scale risks such as pandemics and nuclear war,” the signatories warned.

This came on the heels of another high-profile letter , signed by the likes of Elon Musk and Steve Wozniak, a co-founder of Apple, calling for a six-month moratorium on the development of advanced A.I. systems.

Meanwhile, the Biden administration has urged responsible A.I. innovation, stating that “in order to seize the opportunities” it offers, we “must first manage its risks.” In Congress, Senator Chuck Schumer called for “first of their kind” listening sessions on the potential and risks of A.I., a crash course of sorts from industry executives, academics, civil rights activists and other stakeholders.

The mounting anxiety about A.I. isn’t because of the boring but reliable technologies that autocomplete our text messages or direct robot vacuums to dodge obstacles in our living rooms. It is the rise of artificial general intelligence, or A.G.I., that worries the experts.

A.G.I. doesn’t exist yet, but some believe that the rapidly growing capabilities of OpenAI’s ChatGPT suggest its emergence is near. Sam Altman, a co-founder of OpenAI, has described it as “systems that are generally smarter than humans.” Building such systems remains a daunting — some say impossible — task. But the benefits appear truly tantalizing.

Imagine Roombas, no longer condemned to vacuuming the floors, that evolve into all-purpose robots, happy to brew morning coffee or fold laundry — without ever being programmed to do these things.

Sounds appealing. But should these A.G.I. Roombas get too powerful, their mission to create a spotless utopia might get messy for their dust-spreading human masters. At least we’ve had a good run.

Discussions of A.G.I. are rife with such apocalyptic scenarios. Yet a nascent A.G.I. lobby of academics, investors and entrepreneurs counter that, once made safe, A.G.I. would be a boon to civilization. Mr. Altman, the face of this campaign, embarked on a global tour to charm lawmakers . Earlier this year he wrote that A.G.I. might even turbocharge the economy, boost scientific knowledge and “elevate humanity by increasing abundance.”

This is why, for all the hand-wringing, so many smart people in the tech industry are toiling to build this controversial technology: not using it to save the world seems immoral.

They are beholden to an ideology that views this new technology as inevitable and, in a safe version, as universally beneficial. Its proponents can think of no better alternatives for fixing humanity and expanding its intelligence.

But this ideology — call it A.G.I.-ism — is mistaken. The real risks of A.G.I. are political and won’t be fixed by taming rebellious robots. The safest of A.G.I.s would not deliver the progressive panacea promised by its lobby. And in presenting its emergence as all but inevitable, A.G.I.-ism distracts from finding better ways to augment intelligence.

Unbeknown to its proponents , A.G.I.-ism is just a bastard child of a much grander ideology, one preaching that, as Margaret Thatcher memorably put it, there is no alternative, not to the market.

Rather than breaking capitalism, as Mr. Altman has hinted it could do, A.G.I. — or at least the rush to build it — is more likely to create a powerful (and much hipper) ally for capitalism’s most destructive creed: neoliberalism.

Fascinated with privatization, competition and free trade, the architects of neoliberalism wanted to dynamize and transform a stagnant and labor-friendly economy through markets and deregulation.

Some of these transformations worked, but they came at an immense cost. Over the years, neoliberalism drew many, many critics, who blamed it for the Great Recession and financial crisis, Trumpism, Brexit and much else.

It is not surprising, then, that the Biden administration has distanced itself from the ideology, acknowledging that markets sometimes get it wrong. Foundations, think tanks and academics have even dared to imagine a post-neoliberal future.

Yet neoliberalism is far from dead. Worse, it has found an ally in A.G.I.-ism, which stands to reinforce and replicate its main biases: that private actors outperform public ones (the market bias), that adapting to reality beats transforming it (the adaptation bias ) and that efficiency trumps social concerns (the efficiency bias).

These biases turn the alluring promise behind A.G.I. on its head: Instead of saving the world, the quest to build it will make things only worse. Here is how.

A.G.I. will never overcome the market’s demands for profit.

Remember when Uber, with its cheap rates, was courting cities to serve as their public transportation systems?

It all began nicely, with Uber promising implausibly cheap rides, courtesy of a future with self-driving cars and minimal labor costs. Deep-pocketed investors loved this vision, even absorbing Uber’s multibillion-dollar losses.

But when reality descended , the self-driving cars were still a pipe dream. The investors demanded returns and Uber was forced to raise prices . Users that relied on it to replace public buses and trains were left on the sidewalk.

The neoliberal instinct behind Uber’s business model is that the private sector can do better than the public sector — the market bias.

It’s not just cities and public transit. Hospitals , police departments and even the Pentagon increasingly rely on Silicon Valley to accomplish their missions.

With A.G.I., this reliance will only deepen, not least because A.G.I. is unbounded in its scope and ambition. No administrative or government services would be immune to its promise of disruption.

Moreover, A.G.I. doesn’t even have to exist to lure them in. This, at any rate, is the lesson of Theranos, a start-up that promised to “solve” health care through a revolutionary blood-testing technology and a former darling of America’s elites. Its victims are real, even if its technology never was.

After so many Uber- and Theranos-like traumas, we already know what to expect of an A.G.I. rollout. It will consist of two phases. First, the charm offensive of heavily subsidized services. Then the ugly retrenchment, with the overdependent users and agencies shouldering the costs of making them profitable.

As always, Silicon Valley mavens play down the market’s role. In a recent essay titled “ Why A.I. Will Save the World ,” Marc Andreessen, a prominent tech investor, even proclaims that A.I. “is owned by people and controlled by people, like any other technology.”

Only a venture capitalist can traffic in such exquisite euphemisms. Most modern technologies are owned by corporations. And they — not the mythical “people” — will be the ones that will monetize saving the world.

And are they really saving it? The record, so far, is poor. Companies like Airbnb and TaskRabbit were welcomed as saviors for the beleaguered middle class ; Tesla’s electric cars were seen as a remedy to a warming planet. Soylent, the meal-replacement shake, embarked on a mission to “solve” global hunger, while Facebook vowed to “ solve ” connectivity issues in the Global South. None of these companies saved the world.

A decade ago, I called this solutionism , but “digital neoliberalism” would be just as fitting. This worldview reframes social problems in light of for-profit technological solutions. As a result, concerns that belong in the public domain are reimagined as entrepreneurial opportunities in the marketplace.

A.G.I.-ism has rekindled this solutionist fervor. Last year, Mr. Altman stated that “A.G.I. is probably necessary for humanity to survive” because “our problems seem too big” for us to “solve without better tools.” He’s recently asserted that A.G.I. will be a catalyst for human flourishing.

But companies need profits, and such benevolence, especially from unprofitable firms burning investors’ billions, is uncommon. OpenAI, having accepted billions from Microsoft, has contemplated raising another $100 billion to build A.G.I. Those investments will need to be earned back — against the service’s staggering invisible costs. (One estimate from February put the expense of operating ChatGPT at $700,000 per day.)

Thus, the ugly retrenchment phase, with aggressive price hikes to make an A.G.I. service profitable, might arrive before “abundance” and “flourishing.” But how many public institutions would mistake fickle markets for affordable technologies and become dependent on OpenAI’s expensive offerings by then?

And if you dislike your town outsourcing public transportation to a fragile start-up, would you want it farming out welfare services, waste management and public safety to the possibly even more volatile A.G.I. firms?

A.G.I. will dull the pain of our thorniest problems without fixing them.

Neoliberalism has a knack for mobilizing technology to make society’s miseries bearable. I recall an innovative tech venture from 2017 that promised to improve commuters’ use of a Chicago subway line. It offered rewards to discourage metro riders from traveling at peak times. Its creators leveraged technology to influence the demand side (the riders), seeing structural changes to the supply side (like raising public transport funding) as too difficult. Tech would help make Chicagoans adapt to the city’s deteriorating infrastructure rather than fixing it in order to meet the public’s needs.

This is the adaptation bias — the aspiration that, with a technological wand, we can become desensitized to our plight. It’s the product of neoliberalism’s relentless cheerleading for self-reliance and resilience.

The message is clear: gear up, enhance your human capital and chart your course like a start-up. And A.G.I.-ism echoes this tune. Bill Gates has trumpeted that A.I. can “help people everywhere improve their lives.”

The solutionist feast is only getting started: Whether it’s fighting the next pandemic , the loneliness epidemic or inflation , A.I. is already pitched as an all-purpose hammer for many real and imaginary nails. However, the decade lost to the solutionist folly reveals the limits of such technological fixes.

To be sure, Silicon Valley’s many apps — to monitor our spending, calories and workout regimes — are occasionally helpful. But they mostly ignore the underlying causes of poverty or obesity. And without tackling the causes, we remain stuck in the realm of adaptation, not transformation.

There’s a difference between nudging us to follow our walking routines — a solution that favors individual adaptation — and understanding why our towns have no public spaces to walk on — a prerequisite for a politics-friendly solution that favors collective and institutional transformation.

But A.G.I.-ism, like neoliberalism, sees public institutions as unimaginative and not particularly productive. They should just adapt to A.G.I., at least according to Mr. Altman, who recently said he was nervous about “the speed with which our institutions can adapt” — part of the reason, he added, “of why we want to start deploying these systems really early, while they’re really weak, so that people have as much time as possible to do this.”

But should institutions only adapt? Can’t they develop their own transformative agendas for improving humanity’s intelligence? Or do we use institutions only to mitigate the risks of Silicon Valley’s own technologies?

A.G.I. undermines civic virtues and amplifies trends we already dislike.

A common criticism of neoliberalism is that it has flattened our political life, rearranging it around efficiency. “ The Problem of Social Cost ,” a 1960 article that has become a classic of the neoliberal canon, preaches that a polluting factory and its victims should not bother bringing their disputes to court. Such fights are inefficient — who needs justice, anyway? — and stand in the way of market activity. Instead, the parties should privately bargain over compensation and get on with their business.

This fixation on efficiency is how we arrived at “solving” climate change by letting the worst offenders continue as before. The way to avoid the shackles of regulation is to devise a scheme — in this case, taxing carbon — that lets polluters buy credits to match the extra carbon they emit.

This culture of efficiency, in which markets measure the worth of things and substitute for justice, inevitably corrodes civic virtues.

And the problems this creates are visible everywhere. Academics fret that, under neoliberalism, research and teaching have become commodities. Doctors lament that hospitals prioritize more profitable services such as elective surgery over emergency care. Journalists hate that the worth of their articles is measured in eyeballs .

Now imagine unleashing A.G.I. on these esteemed institutions — the university, the hospital, the newspaper — with the noble mission of “fixing” them. Their implicit civic missions would remain invisible to A.G.I., for those missions are rarely quantified even in their annual reports — the sort of materials that go into training the models behind A.G.I.

After all, who likes to boast that his class on Renaissance history got only a handful of students? Or that her article on corruption in some faraway land got only a dozen page views? Inefficient and unprofitable, such outliers miraculously survive even in the current system. The rest of the institution quietly subsidizes them, prioritizing values other than profit-driven “efficiency.”

Will this still be the case in the A.G.I. utopia? Or will fixing our institutions through A.G.I. be like handing them over to ruthless consultants? They, too, offer data-bolstered “solutions” for maximizing efficiency. But these solutions often fail to grasp the messy interplay of values, missions and traditions at the heart of institutions — an interplay that is rarely visible if you only scratch their data surface.

In fact, the remarkable performance of ChatGPT-like services is, by design, a refusal to grasp reality at a deeper level, beyond the data’s surface. So whereas earlier A.I. systems relied on explicit rules and required someone like Newton to theorize gravity — to ask how and why apples fall — newer systems like A.G.I. simply learn to predict gravity’s effects by observing millions of apples fall to the ground.

However, if all that A.G.I. sees are cash-strapped institutions fighting for survival, it may never infer their true ethos. Good luck discerning the meaning of the Hippocratic oath by observing hospitals that have been turned into profit centers.

Margaret Thatcher’s other famous neoliberal dictum was that “ there is no such thing as society .”

The A.G.I. lobby unwittingly shares this grim view. For them, the kind of intelligence worth replicating is a function of what happens in individuals’ heads rather than in society at large.

But human intelligence is as much a product of policies and institutions as it is of genes and individual aptitudes. It’s easier to be smart on a fellowship in the Library of Congress than while working several jobs in a place without a bookstore or even decent Wi-Fi.

It doesn’t seem all that controversial to suggest that more scholarships and public libraries will do wonders for boosting human intelligence. But for the solutionist crowd in Silicon Valley, augmenting intelligence is primarily a technological problem — hence the excitement about A.G.I.

However, if A.G.I.-ism really is neoliberalism by other means, then we should be ready to see fewer — not more — intelligence-enabling institutions. After all, they are the remnants of that dreaded “society” that, for neoliberals, doesn’t really exist. A.G.I.’s grand project of amplifying intelligence may end up shrinking it.

Because of such solutionist bias, even seemingly innovative policy ideas around A.G.I. fail to excite. Take the recent proposal for a “ Manhattan Project for A.I. Safety .” This is premised on the false idea that there’s no alternative to A.G.I.

But wouldn’t our quest for augmenting intelligence be far more effective if the government funded a Manhattan Project for culture and education and the institutions that nurture them instead?

Without such efforts, the vast cultural resources of our existing public institutions risk becoming mere training data sets for A.G.I. start-ups, reinforcing the falsehood that society doesn’t exist.

Depending on how (and if) the robot rebellion unfolds, A.G.I. may or may not prove an existential threat. But with its antisocial bent and its neoliberal biases, A.G.I.-ism already is: We don’t need to wait for the magic Roombas to question its tenets.

Evgeny Morozov , the author of “To Save Everything, Click Here: The Folly of Technological Solutionism,” is the founder and publisher of The Syllabus and the host of the podcast “The Santiago Boys .”

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

Follow The New York Times Opinion section on Facebook , Twitter (@NYTopinion) and Instagram .

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Major new report explains the risks and rewards of artificial intelligence

AI has begun to permeate every aspect of our lives. Image:  Unsplash/Hitesh Choudhary

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Liz sonenberg.

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Stay up to date:, artificial intelligence.

• A new report has just been released, highlighting the changes in AI over the last 5 years, and predicted future trends.
• It was co-written by people across the world, with backgrounds in computer science, engineering, law, political science, policy, sociology and economics.
• In the last 5 years, AI has become an increasing part of our lives, revolutionizing a number of industries, but is still not free from risk.

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.

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 World Economic Forum was the first to draw the world’s attention to the Fourth Industrial Revolution, the current period of unprecedented change driven by rapid technological advances. Policies, norms and regulations have not been able to keep up with the pace of innovation, creating a growing need to fill this gap.

The Forum established the Centre for the Fourth Industrial Revolution Network in 2017 to ensure that new and emerging technologies will help—not harm—humanity in the future. Headquartered in San Francisco, the network launched centres in China, India and Japan in 2018 and is rapidly establishing locally-run Affiliate Centres in many countries around the world.

The global network is working closely with partners from government, business, academia and civil society to co-design and pilot agile frameworks for governing new and emerging technologies, including artificial intelligence (AI) , autonomous vehicles , blockchain , data policy , digital trade , drones , internet of things (IoT) , precision medicine and environmental innovations .

Learn more about the groundbreaking work that the Centre for the Fourth Industrial Revolution Network is doing to prepare us for the future.

Want to help us shape the Fourth Industrial Revolution? Contact us to find out how you can become a member or partner.

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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World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

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Second in a four-part series that taps the expertise of the Harvard community to examine the promise and potential pitfalls of the rising age of artificial intelligence and machine learning , and how to humanize them .

For decades, artificial intelligence, or AI, was the engine of high-level STEM research. Most consumers became aware of the technology’s power and potential through internet platforms like Google and Facebook, and retailer Amazon. Today, AI is essential across a vast array of industries, including health care, banking, retail, and manufacturing.

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But its game-changing promise to do things like improve efficiency, bring down costs, and accelerate research and development has been tempered of late with worries that these complex, opaque systems may do more societal harm than economic good. With virtually no U.S. government oversight, private companies use AI software to make determinations about health and medicine, employment, creditworthiness, and even criminal justice without having to answer for how they’re ensuring that programs aren’t encoded, consciously or unconsciously, with structural biases.

Its growing appeal and utility are undeniable. Worldwide business spending on AI is expected to hit $50 billion this year and $110 billion annually by 2024, even after the global economic slump caused by the COVID-19 pandemic, according to a forecast released in August by technology research firm IDC. Retail and banking industries spent the most this year, at more than $5 billion each. The company expects the media industry and federal and central governments will invest most heavily between 2018 and 2023 and predicts that AI will be “the disrupting influence changing entire industries over the next decade.”

“Virtually every big company now has multiple AI systems and counts the deployment of AI as integral to their strategy,” said Joseph Fuller , professor of management practice at Harvard Business School, who co-leads Managing the Future of Work , a research project that studies, in part, the development and implementation of AI, including machine learning, robotics, sensors, and industrial automation, in business and the work world.

Early on, it was popularly assumed that the future of AI would involve the automation of simple repetitive tasks requiring low-level decision-making. But AI has rapidly grown in sophistication, owing to more powerful computers and the compilation of huge data sets. One branch, machine learning, notable for its ability to sort and analyze massive amounts of data and to learn over time, has transformed countless fields, including education.

Firms now use AI to manage sourcing of materials and products from suppliers and to integrate vast troves of information to aid in strategic decision-making, and because of its capacity to process data so quickly, AI tools are helping to minimize time in the pricey trial-and-error of product development — a critical advance for an industry like pharmaceuticals, where it costs $1 billion to bring a new pill to market, Fuller said.

Health care experts see many possible uses for AI, including with billing and processing necessary paperwork. And medical professionals expect that the biggest, most immediate impact will be in analysis of data, imaging, and diagnosis. Imagine, they say, having the ability to bring all of the medical knowledge available on a disease to any given treatment decision.

In employment, AI software culls and processes resumes and analyzes job interviewees’ voice and facial expressions in hiring and driving the growth of what’s known as “hybrid” jobs. Rather than replacing employees, AI takes on important technical tasks of their work, like routing for package delivery trucks, which potentially frees workers to focus on other responsibilities, making them more productive and therefore more valuable to employers.  

“It’s allowing them to do more stuff better, or to make fewer errors, or to capture their expertise and disseminate it more effectively in the organization,” said Fuller, who has studied the effects and attitudes of workers who have lost or are likeliest to lose their jobs to AI.

“Can smart machines outthink us, or are certain elements of human judgment indispensable in deciding some of the most important things in life?”

— Michael Sandel, political philosopher and Anne T. and Robert M. Bass Professor of Government

Though automation is here to stay, the elimination of entire job categories, like highway toll-takers who were replaced by sensors because of AI’s proliferation, is not likely, according to Fuller.

“What we’re going to see is jobs that require human interaction, empathy, that require applying judgment to what the machine is creating [will] have robustness,” he said.

While big business already has a huge head start, small businesses could also potentially be transformed by AI, says Karen Mills ’75, M.B.A. ’77, who ran the U.S. Small Business Administration from 2009 to 2013. With half the country employed by small businesses before the COVID-19 pandemic, that could have major implications for the national economy over the long haul.

Rather than hamper small businesses, the technology could give their owners detailed new insights into sales trends, cash flow, ordering, and other important financial information in real time so they can better understand how the business is doing and where problem areas might loom without having to hire anyone, become a financial expert, or spend hours laboring over the books every week, Mills said.

One area where AI could “completely change the game” is lending, where access to capital is difficult in part because banks often struggle to get an accurate picture of a small business’s viability and creditworthiness.

“It’s much harder to look inside a business operation and know what’s going on” than it is to assess an individual, she said.

Information opacity makes the lending process laborious and expensive for both would-be borrowers and lenders, and applications are designed to analyze larger companies or those who’ve already borrowed, a built-in disadvantage for certain types of businesses and for historically underserved borrowers, like women and minority business owners, said Mills, a senior fellow at HBS.

But with AI-powered software pulling information from a business’s bank account, taxes, and online bookkeeping records and comparing it with data from thousands of similar businesses, even small community banks will be able to make informed assessments in minutes, without the agony of paperwork and delays, and, like blind auditions for musicians, without fear that any inequity crept into the decision-making.

“All of that goes away,” she said.

A veneer of objectivity

Not everyone sees blue skies on the horizon, however. Many worry whether the coming age of AI will bring new, faster, and frictionless ways to discriminate and divide at scale.

“Part of the appeal of algorithmic decision-making is that it seems to offer an objective way of overcoming human subjectivity, bias, and prejudice,” said political philosopher Michael Sandel , Anne T. and Robert M. Bass Professor of Government. “But we are discovering that many of the algorithms that decide who should get parole, for example, or who should be presented with employment opportunities or housing … replicate and embed the biases that already exist in our society.”

“If we’re not thoughtful and careful, we’re going to end up with redlining again.”

— Karen Mills, senior fellow at the Business School and head of the U.S. Small Business Administration from 2009 to 2013

AI presents three major areas of ethical concern for society: privacy and surveillance, bias and discrimination, and perhaps the deepest, most difficult philosophical question of the era, the role of human judgment, said Sandel, who teaches a course in the moral, social, and political implications of new technologies.

“Debates about privacy safeguards and about how to overcome bias in algorithmic decision-making in sentencing, parole, and employment practices are by now familiar,” said Sandel, referring to conscious and unconscious prejudices of program developers and those built into datasets used to train the software. “But we’ve not yet wrapped our minds around the hardest question: Can smart machines outthink us, or are certain elements of human judgment indispensable in deciding some of the most important things in life?”

Panic over AI suddenly injecting bias into everyday life en masse is overstated, says Fuller. First, the business world and the workplace, rife with human decision-making, have always been riddled with “all sorts” of biases that prevent people from making deals or landing contracts and jobs.

When calibrated carefully and deployed thoughtfully, resume-screening software allows a wider pool of applicants to be considered than could be done otherwise, and should minimize the potential for favoritism that comes with human gatekeepers, Fuller said.

Sandel disagrees. “AI not only replicates human biases, it confers on these biases a kind of scientific credibility. It makes it seem that these predictions and judgments have an objective status,” he said.

In the world of lending, algorithm-driven decisions do have a potential “dark side,” Mills said. As machines learn from data sets they’re fed, chances are “pretty high” they may replicate many of the banking industry’s past failings that resulted in systematic disparate treatment of African Americans and other marginalized consumers.

“If we’re not thoughtful and careful, we’re going to end up with redlining again,” she said.

A highly regulated industry, banks are legally on the hook if the algorithms they use to evaluate loan applications end up inappropriately discriminating against classes of consumers, so those “at the top levels” in the field are “very focused” right now on this issue, said Mills, who closely studies the rapid changes in financial technology, or “fintech.”

“They really don’t want to discriminate. They want to get access to capital to the most creditworthy borrowers,” she said. “That’s good business for them, too.”

Oversight overwhelmed

Given its power and expected ubiquity, some argue that the use of AI should be tightly regulated. But there’s little consensus on how that should be done and who should make the rules.

Thus far, companies that develop or use AI systems largely self-police, relying on existing laws and market forces, like negative reactions from consumers and shareholders or the demands of highly-prized AI technical talent to keep them in line.

“There’s no businessperson on the planet at an enterprise of any size that isn’t concerned about this and trying to reflect on what’s going to be politically, legally, regulatorily, [or] ethically acceptable,” said Fuller.

Firms already consider their own potential liability from misuse before a product launch, but it’s not realistic to expect companies to anticipate and prevent every possible unintended consequence of their product, he said.

Few think the federal government is up to the job, or will ever be.

“The regulatory bodies are not equipped with the expertise in artificial intelligence to engage in [oversight] without some real focus and investment,” said Fuller, noting the rapid rate of technological change means even the most informed legislators can’t keep pace. Requiring every new product using AI to be prescreened for potential social harms is not only impractical, but would create a huge drag on innovation.

“I wouldn’t have a central AI group that has a division that does cars, I would have the car people have a division of people who are really good at AI.”

— Jason Furman, a professor of the practice of economic policy at the Kennedy School and a former top economic adviser to President Barack Obama

Jason Furman , a professor of the practice of economic policy at Harvard Kennedy School, agrees that government regulators need “a much better technical understanding of artificial intelligence to do that job well,” but says they could do it.

Existing bodies like the National Highway Transportation Safety Association, which oversees vehicle safety, for example, could handle potential AI issues in autonomous vehicles rather than a single watchdog agency, he said.

“I wouldn’t have a central AI group that has a division that does cars, I would have the car people have a division of people who are really good at AI,” said Furman, a former top economic adviser to President Barack Obama.

Though keeping AI regulation within industries does leave open the possibility of co-opted enforcement, Furman said industry-specific panels would be far more knowledgeable about the overarching technology of which AI is simply one piece, making for more thorough oversight.

While the European Union already has rigorous data-privacy laws and the European Commission is considering a formal regulatory framework for ethical use of AI, the U.S. government has historically been late when it comes to tech regulation.

“I think we should’ve started three decades ago, but better late than never,” said Furman, who thinks there needs to be a “greater sense of urgency” to make lawmakers act.

Business leaders “can’t have it both ways,” refusing responsibility for AI’s harmful consequences while also fighting government oversight, Sandel maintains.

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“The problem is these big tech companies are neither self-regulating, nor subject to adequate government regulation. I think there needs to be more of both,” he said, later adding: “We can’t assume that market forces by themselves will sort it out. That’s a mistake, as we’ve seen with Facebook and other tech giants.”

Last fall, Sandel taught “ Tech Ethics ,” a popular new Gen Ed course with Doug Melton, co-director of Harvard’s Stem Cell Institute. As in his legendary “Justice” course, students consider and debate the big questions about new technologies, everything from gene editing and robots to privacy and surveillance.

“Companies have to think seriously about the ethical dimensions of what they’re doing and we, as democratic citizens, have to educate ourselves about tech and its social and ethical implications — not only to decide what the regulations should be, but also to decide what role we want big tech and social media to play in our lives,” said Sandel.

Doing that will require a major educational intervention, both at Harvard and in higher education more broadly, he said.

“We have to enable all students to learn enough about tech and about the ethical implications of new technologies so that when they are running companies or when they are acting as democratic citizens, they will be able to ensure that technology serves human purposes rather than undermines a decent civic life.”

Next: The AI revolution in medicine may lift personalized treatment, fill gaps in access to care, and cut red tape. Yet risks abound.

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To Err is Not Human: The Dangers of AI-assisted Academic Writing

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Table of Contents

Artificial intelligence (AI)-powered writing tools are becoming increasingly popular among researchers. AI tools can improve several important aspects of writing, such as readability, grammar, spelling, and tone, providing authors with a competitive edge when drafting grant proposals and academic articles. In recent years, there has also been an increase in the use of “Generative AI,” which can produce write-ups that appear to have been drafted by humans. However, despite AI’s enormous potential in academic writing, there are several significant pitfalls in its use. 

Inauthentic Sources

AI tools are built on rapidly evolving deep learning algorithms that fetch answers to your queries or “prompts”. Owing to advances in computation, and the rapid growth in the amount of data that algorithms can access, these tools are often accurate in their answers. However, at times AI can make mistakes and give you inaccurate data. What is worrying is, this data may look authentic at a first glance and increase the risk of getting incorporated in research articles. Failing to scrutinise information and data sources provided by AI can therefore impair scientific credibility and trigger a chain of falsification in the research community. 

Why Human Supervision Is Advisable

AI-generated output is frequently generic, matched with synonyms, and may not be able to critically analyse the scientific context when writing manuscripts. 

Consider the following example, where the AI ‘ChatGPT’ was used to generate a one-line summary of the following sentences:

The malaria parasite Plasmodium falciparum has an organelle，the  apicoplast, which contains its own genome.

This organelle is significant in the Plasmodium’s lifecycle, but we are yet to thoroughly understand the regulation of apicoplast gene expression.

The following is a human-generated one-line summary:

The malaria parasite Plasmodium falciparum has an organelle that is significant in its lifecycle called an apicoplast, which contains its own genome —but the regulation of apicoplast gene expression is poorly understood.

On the other hand, the AI-generated summary is as follows:

The malaria parasite Plasmodium falciparum has an apicoplast, an organelle with its own genome , significant in its life cycle , yet its gene expression regulation remains poorly understood.

In the AI-generated text, it is not clear what ‘its’ refers to in each instance of because it could either refer to Plasmodium falciparum or it could refer to the apicoplast. Moreover, while the expression ‘gene expression regulation’ is technically correct, the sentence structure and writing style is superior if you write ‘regulation of gene expression’.

This is why we need humans to supervise AI bots and verify the accuracy of all information submitted for publication. We request that authors who have used AI or AI-assisted tools include a declaration statement at the end of their manuscript where they specify the tool and the reason for using it.

An example of AI-generated text using the software ChatGPT

Data Leakage

AI is now an integral part of scientific research. From data collection to manuscript preparation, AI provides ways to improve and expedite every step of the research process. However, to function, AI needs access to data and adequate computing power to process them efficiently. One way in which many AI applications meet these requirements is by having large, distributed databases and dividing the labour among several individual computers. These AI applications need to stay connected to the internet to work. Therefore, researchers who upload academic content from unpublished papers to platforms like ChatGPT are at a higher risk of data leakage and privacy violations.

To address this issue, governments in various countries have decided to implement policies. Italy, for example, banned ChatGPT in April 2023 due to privacy concerns, but later reinstated the AI app with a new privacy policy that verifies users’ ages. The European Union is also developing a new policy that will regulate AI platforms such as ChatGPT and Google Bard. The US Congress and India’s IT department have also hinted at developing new frameworks for AI compliance with safety standards.

Elsevier also strives to minimize the risk of data leakage. Our policy on the use of AI and AI-assisted technologies in scientific writing aims to provide authors, readers, reviewers, editors, and contributors with more transparency and guidance. 

Legal and Ethical Restrictions on Use

Most publishers allow the use of AI writing tools during manuscript preparation as long as it is used to improve, and not wholly generate, sentences. Elsevier’s policy also allows authors to use AI tools to improve the readability and language of their submissions but emphasises that the generated output is ultimately reviewed by the author(s) to avoid mistakes. Moreover, we require authors to keep us informed and acknowledge the use of AI-assisted writing during the submission process. Information regarding this is included in the published article in the interest of transparency. Visit this resource for more details.

We must know that AI programs are not considered authors of a manuscript, and since they do not receive the credit, they also do not bear responsibility. Authors are solely responsible for any mistakes in AI-assisted writing that find their way into manuscripts.

AI-assisted writing is here to stay. While it is advisable to familiarise oneself with AI writing technology, it is equally advisable to be aware of its risks and limitations. 

Need safe and reliable writing assistance? Experts at Elsevier Author Services can assist you in every step of the manuscript preparation process. Contact us for a full list of services and any additional information.

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Exploring the Negative Impact of Artificial Intelligence in Education Sector

Artificial Intelligence (AI) has made significant advancements in recent years, revolutionizing various industries and sectors, including education. It is important to address these concerns to ensure that the benefits of AI in education are maximized while minimizing any potential negative impacts. While AI has the potential to enhance and transform education, it also poses several negative impacts that need to be addressed.

Two major concerns associated with the integration of AI in education are ethics and privacy. AI systems have access to vast amounts of data, including personal information about students. This raises questions about data security and the potential misuse of sensitive information. Additionally, AI algorithms can be biased, leading to unfair decision-making processes. Emphasis should be placed on developing and implementing ethical frameworks and regulations to ensure the responsible use of AI in education.

Another negative impact of AI in education is the potential disruption it may cause to traditional teaching methods and roles. The increasing use of AI-powered tools in education has raised concerns about the potential replacement of teachers by automated systems, resulting in job loss and a diminished human touch in the learning experience. Additionally, the growing reliance on AI systems for tasks like grading and personalized learning may impede students’ critical thinking and problem-solving skills. Therefore, it is essential to strike a balance between AI and human interaction in education to preserve the fundamental qualities of teaching and learning.

In order to avoid this negative consequence of AI, it is important to ensure that access to these resources is available to all students regardless of their socio-economic background. AI in education has the potential to provide equal learning opportunities for all, but there is a risk that it may widen existing educational inequalities. Access to AI-powered resources and technologies may be limited to privileged schools or individuals, creating a digital divide between different socio-economic groups. Efforts must be made to ensure equal access and distribution of AI resources to prevent further disparities in education.

AI brings numerous benefits to the field of education, but it also presents several negative impacts that need to be addressed. To maximize the positive impact of AI on education while minimizing its negative consequences, it is necessary to address the ethical concerns surrounding data privacy and biased algorithms, potential job loss for teachers, dependence on AI systems, and widening educational inequalities. A thoughtful and proactive approach can help us harness AI’s potential for the betterment of education.

The Era of Artificial Intelligence

As we enter the era of artificial intelligence (AI), there is both excitement and concern. AI has the potential to revolutionize various aspects of our lives, from healthcare to transportation, but it also brings about challenges and negative consequences.

One major concern surrounding AI is the potential for increased inequality. As AI technologies advance, those with access may gain a competitive advantage over those without, potentially widening the gap between the rich and poor and creating disparities in educational opportunities.

Additionally, traditional educational systems may struggle to keep up with the rapid pace of AI evolution, leading to outdated curricula. The education system may be teaching students skills that are no longer applicable in the current job market, resulting in a mismatch between education and industry needs.

Additionally, the use of AI-powered systems and devices raises concerns about data security and the protection of individual privacy, as personal information and data are being collected and analyzed on a massive scale.

The automation of tasks previously performed by humans is a consequence of AI. While this can lead to increased efficiency and productivity, it also raises concerns about job loss. As AI technology continues to improve, many jobs currently performed by humans may be automated, leading to unemployment and economic disruption.

The era of artificial intelligence presents the challenge of dependence on AI. As AI systems become more integrated into our lives, there is a risk of over-reliance on them, which can lead to a loss of critical thinking skills and a decreased ability to make independent decisions.

One major concern with AI is the potential for bias. AI algorithms are solely dependent on the quality of the data they are trained on. If the data used is biased or flawed, the AI system will also be biased, leading to unfair treatment and discrimination in various domains, including education. If the data used is biased or flawed, the AI system will also be biased, leading to unfair treatment and discrimination in various domains, including education. If the data used is biased or flawed, the AI system will also be biased, leading to unfair treatment and discrimination in various domains, including education.

In conclusion, while the era of AI holds immense potential for positive advancements, we must also address the potential negative impacts. It is important to approach the era of artificial intelligence with caution to ensure that the benefits outweigh the drawbacks. This includes addressing issues such as inequality, outdated curricula, privacy concerns, and job loss.

Limits and Concerns

Although AI has the potential to revolutionize education, there are also concerns surrounding its implementation. One significant concern is the potential job loss that may occur as AI and automation take over certain tasks traditionally performed by teachers and educators. This could lead to unemployment and a lack of human interaction in the learning process.

Another major concern is privacy when it comes to AI in education. As AI systems collect and analyze vast amounts of data on students, there is a risk of this information being misused or falling into the wrong hands. Therefore, safeguarding student privacy and ensuring ethical data practices are vital in the integration of AI in education.

Additionally, there is a concern about the over-reliance on AI and technology in the classroom, which could lead to a lack of critical thinking skills and problem-solving abilities among students. It is crucial to strike a balance between utilizing AI as a tool and fostering the growth of human intelligence.

Another concern is the potential automation of education, where AI systems could replace human teachers completely. This raises ethical questions regarding the quality of education and the social implications of removing human mentors from the learning process. The role of teachers should not be underestimated, and AI should be treated as a supplement rather than a replacement for human educators.

The integration of AI in education can disrupt traditional teaching methods and necessitate an updated curriculum. Educators must adapt and keep up with the latest technologies and teaching approaches as AI systems constantly evolve. Failure to do so may result in an outdated curriculum that does not effectively prepare students for the future.

Additionally, there is a concern about bias in AI algorithms used in education. Biased data used to train AI systems can perpetuate societal inequalities and discrimination in the learning environment. Ensuring fairness and addressing biases in AI algorithms is crucial to creating an inclusive and equitable educational experience for all students.

The Role of AI in Education

Artificial intelligence (AI) has the potential to revolutionize education by addressing various challenges that have long plagued the education system. One significant issue is the presence of outdated curriculum, which fails to keep up with the changing needs of society and the job market. AI can play a crucial role in ensuring that the curriculum remains relevant and up-to-date, providing students with the necessary knowledge and skills that are in demand.

However, the integration of AI in education has raised concerns about potential job loss among teachers and other educational professionals. While AI can automate certain tasks and improve efficiency, it is important to strike a balance between human interaction and technology to ensure the best learning experience for students.

Privacy is a critical aspect that requires attention when implementing AI in education. While data collection and analysis can provide valuable insights into student performance and learning patterns, it is essential to prioritize the protection of student data and ensure its ethical and secure use.

Additionally, AI in education has the potential to worsen existing inequalities. Efforts should be made to promote equal access to AI-powered educational resources and ensure that they are used inclusively to benefit all students, especially those from disadvantaged backgrounds who may have limited access. This will help bridge the digital divide. Efforts should be made to promote equal access to AI-powered educational resources and ensure that they are used inclusively to benefit all students, especially those from disadvantaged backgrounds who may have limited access.

The integration of AI in education has the potential to disrupt traditional teaching and learning methods. AI-powered tools can personalize learning experiences, adapting to individual student needs. This shift challenges the conventional one-size-fits-all approach, allowing for more effective and efficient learning.

While AI can bring numerous benefits to education, it is important to recognize its limitations and avoid excessive dependence. Human interaction and critical thinking skills remain essential aspects of education that cannot be replaced by AI alone. Therefore, educators should use AI as a supportive tool rather than relying solely on it.

Finally, it is crucial to carefully consider the ethical implications of AI in education. AI algorithms may introduce bias into the educational process, reinforcing societal stereotypes or prejudices. It is crucial to ensure that AI systems are developed and used responsibly, with a focus on fairness and unbiased outcomes.

In conclusion, AI has the potential to transform education by addressing outdated curriculum and personalizing learning experiences. However, it is essential to address concerns such as job loss, privacy, inequality, disruption, dependence, ethics, and bias to ensure that AI is utilized in a way that benefits all students and promotes a fair and inclusive educational environment.

Decreased Human Interaction

One of the negative impacts of AI on education is the decreased human interaction. With the increasing use of AI technologies in classrooms, there is a growing concern that students will have limited opportunities for face-to-face interaction with their peers and teachers.

Ethics, inequality, outdated curriculum, bias, automation, disruption, dependence, and job loss are some of the factors that contribute to this decrease in human interaction. AI technologies, while efficient and capable of improving educational outcomes, lack the emotional intelligence and empathy that are fundamental to human interaction.

AI tools such as chatbots and virtual assistants can provide quick answers and assistance to students, but they cannot replace the benefits of interacting with a real teacher or engaging in group discussions with classmates. This limited human interaction can hinder the development of crucial social and communication skills that are necessary for success in life.

Furthermore, the use of AI in education can deepen existing inequalities. Students from disadvantaged backgrounds may not have equal access to AI technologies, exacerbating the digital divide and widening the educational gap. This inequality can further isolate students and limit their opportunities for meaningful human interaction.

Another concern is that AI tools can reinforce biases and perpetuate inequality. AI algorithms rely on data, and if the data used to train these algorithms is biased, the results can be skewed. This can lead to unfair evaluations and discrimination, limiting students’ ability to engage in open and unbiased discussions.

In addition, excessive reliance on AI technologies in education can lead to a disconnection from the real world. Students may become dependent on AI tools for problem-solving and critical thinking, overlooking the importance of hands-on experiences and practical application of knowledge.

Moreover, the widespread use of AI in education can disrupt the teaching profession. Teachers may face job loss or reduced roles as AI takes over certain tasks, further diminishing the opportunities for human interaction in the classroom.

In conclusion, while AI has the potential to improve education, its negative impact on human interaction cannot be overlooked. It is crucial to find a balance between AI and human involvement in the educational process to ensure that students receive a well-rounded education that includes meaningful human interaction and socialization.

Automation and Job Displacement

As artificial intelligence continues to advance, it is inevitable that automation will have a significant impact on the job market, including the field of education. While automation can bring efficiency and convenience, it also poses challenges and risks that need to be considered.

Inequality and Disruption

One of the main concerns with AI in education is the potential for increased inequality. As more tasks become automated, individuals with the necessary technical skills may thrive, while others who lack those skills could be left behind. This could exacerbate existing social and economic inequalities.

Additionally, automation can disrupt traditional roles and job structures within the education sector. Teachers, for example, may find themselves displaced or required to adapt to new roles and responsibilities.

Ethics and Privacy

The use of AI in education raises important ethical questions. For instance, there is a concern that AI algorithms could be biased or discriminatory, resulting in unequal treatment of students. It is crucial to ensure that AI systems are transparent, fair, and free from bias.

Moreover, the integration of AI in education raises privacy concerns. Personal data collected from students, such as their learning preferences and behaviors, could be misused or mishandled. It is essential to establish strict regulations and safeguards to protect students’ privacy.

Outdated Curriculum and Job Loss

Another negative impact of AI in education is the potential reinforcement of outdated curriculum. AI systems might perpetuate existing knowledge gaps and limit the scope of learning. This could hinder students’ abilities to adapt to the ever-changing world and acquire critical thinking skills.

Furthermore, automation in education may lead to job losses. As tasks become automated, certain positions, such as administrative roles, could be eliminated. This could have a significant impact on the employment landscape within the education sector.

In conclusion, while AI has the potential to bring numerous benefits to education, it also has its drawbacks. It is crucial to address issues of inequality, disruption, ethics, privacy, outdated curriculum, job loss, bias, and dependence on AI to ensure a balanced and equitable integration of AI in the field of education.

Biased Algorithms and Discrimination

One of the biggest concerns regarding the negative impact of AI on education is the potential for biased algorithms and discrimination. As AI systems become increasingly integrated into the educational system, there is a risk that these algorithms may perpetuate and even amplify existing biases and inequalities.

This issue is closely linked to the problem of an outdated curriculum. If AI systems are trained on data that is itself biased, they may further reinforce and perpetuate discriminatory practices. For example, if an AI system is trained on historical data that reflects gender or racial biases, it could inadvertently replicate these biases in its decision-making processes.

Another concern is the dependence on AI for educational decision-making. As AI systems are increasingly used to automate and streamline various aspects of education, there is a risk that students and teachers may become overly reliant on these systems. This dependence can lead to a lack of critical thinking skills and a reduced ability to make independent judgments.

Furthermore, the use of AI in education raises significant privacy concerns. AI systems collect and analyze vast amounts of data about students, including personal information and educational performance. There is a risk that this data could be misused or accessed by unauthorized parties, leading to potential privacy breaches.

Moreover, the automation of certain educational tasks and the potential job loss that may result can exacerbate inequalities. For example, if certain teaching or administrative tasks are automated, it may lead to a reduction in the number of jobs available for teachers or support staff, particularly in underprivileged communities.

Finally, biased algorithms and discrimination in AI can directly contribute to educational inequality. If certain groups of students are systematically disadvantaged or discriminated against by AI systems, it can further widen the educational attainment gap between different socioeconomic and demographic groups.

In conclusion, the negative impact of AI on education is compounded by the potential for biased algorithms and discrimination. It is crucial to address these issues and ensure that AI systems are designed and implemented in a way that promotes fairness, transparency, and equal opportunities for all students.

Privacy and Security Risks

As AI becomes increasingly integrated into educational systems, privacy and security concerns have arisen. The collection and analysis of vast amounts of data regarding students, teachers, and educational practices raises ethical questions about the potential misuse of this information.

One of the main disruptions caused by AI in education is the potential invasion of student privacy. With AI-powered systems constantly monitoring and analyzing student behavior, there is a risk of sensitive information being accessed and exploited. This can range from personal data such as names and addresses to more intimate details like learning disabilities or behavioral patterns.

Ethics and Inequality

The use of AI also raises ethical concerns regarding the fairness of educational opportunities. With AI systems being primarily designed by certain groups or organizations, it can inadvertently perpetuate existing biases and inequalities in education. For example, if the AI algorithms are trained on data that is biased against certain groups, it may reinforce discriminatory practices or educational inequalities.

Outdated Curriculum and Automation

AI’s automation capabilities have the potential to disrupt traditional educational systems by rendering certain roles and tasks obsolete. This can lead to job losses and a dependence on AI systems, which can further exacerbate inequality. Additionally, if AI is used to deliver educational content, there is a risk of an outdated or narrow curriculum being propagated, limiting students’ exposure to diverse perspectives and knowledge.

In conclusion, while AI has the potential to revolutionize education, it also brings with it privacy and security risks. Addressing these concerns, along with ensuring ethical practices and avoiding the perpetuation of inequality, is crucial to harnessing the full potential of AI in education.

The Educational System Challenges

As AI becomes more prevalent in education, it brings along several challenges that the educational system must address. These challenges include:

• Inequality: AI in education can widen the gap between students from different socio-economic backgrounds, as those with more resources may have better access to AI-powered tools and resources.
• Job loss: The increased use of automation and AI in education may lead to a reduction in the number of teaching positions available, potentially resulting in job losses for educators.
• Dependence: Increased reliance on AI systems and technologies may make students dependent on technology for learning, diminishing their ability to think critically and independently.
• Disruption: The integration of AI into the educational system can disrupt traditional teaching methods and practices, requiring educators to adapt and develop new skills.
• Privacy: The use of AI in education raises concerns about student data privacy and security, as AI systems collect and analyze large amounts of personal information.
• Ethics: AI-powered educational tools may raise ethical concerns, such as the difficulty in programming AI systems to make fair and unbiased decisions.
• Automation: AI can automate routine tasks in education, but it may also lead to a lack of personal interaction between students and educators.
• Bias: AI algorithms used in education can be prone to bias, potentially perpetuating existing inequalities and prejudices in the educational system.

Addressing these challenges requires careful consideration of the ethical, social, and pedagogical implications of AI in education, as well as the development of policies and safeguards to mitigate any potential negative impacts.

Inequality and Accessibility

The advancement of AI in education has the potential to exacerbate inequality and create barriers to accessibility. As AI continues to replace certain tasks traditionally performed by humans, there is a concern that job loss will disproportionately affect individuals who are already marginalized or economically disadvantaged. This can further widen the gap between the rich and the poor, leading to increased inequality in society.

Additionally, as education becomes more dependent on AI and automation, there is a risk that students may become overly reliant on technology, diminishing their ability to think critically and problem-solve independently. The disruption caused by the rapid integration of AI into the education system may also lead to an outdated curriculum that fails to equip students with the necessary skills for the future workforce.

Moreover, the use of AI in education raises ethical concerns regarding the potential for bias in algorithms and data. AI systems are only as good as the data they are trained on, and if that data is biased or incomplete, it can perpetuate existing inequalities in education. Furthermore, the collection and analysis of personal data to personalize education through AI can raise privacy concerns, particularly when it comes to sensitive information about students.

Addressing Inequality and Accessibility

To mitigate the negative impact of AI on education, it is crucial to prioritize accessibility and equitable distribution of resources. This includes ensuring that AI technologies are affordable and accessible to all students, regardless of their socioeconomic background. Additionally, efforts should be made to close the digital divide and provide equal access to reliable internet connectivity and devices.

Furthermore, educational institutions and policymakers must work together to update curricula and teaching methodologies to align with the changing needs of the digital age. This includes incorporating critical thinking, creativity, and problem-solving skills that cannot be easily automated by AI.

Finally, there is a need for comprehensive regulations and ethical frameworks to govern the use of AI in education. This includes holding AI systems accountable for bias and ensuring transparency in the algorithms used to make educational decisions. Additionally, student data privacy should be protected, and clear guidelines should be established to prevent misuse or unethical practices.

Loss of Critical Thinking Skills

The widespread introduction of AI in education has raised concerns about the potential loss of critical thinking skills among students. As AI takes over tasks that traditionally required human intelligence and decision-making, students may become overly reliant on AI systems to provide solutions and answers, leading to a reduced ability to think critically or solve complex problems independently.

One major concern is the impact of job loss on critical thinking skills. As AI automates repetitive and routine tasks, jobs that require critical thinking and problem-solving skills are at risk of being replaced. This could lead to a decreased emphasis on developing these skills in educational settings, as students may prioritize learning technical skills for future employment.

Ethics also play a role in the loss of critical thinking skills in the context of AI. Without a proper understanding of the ethical implications associated with AI systems, students may struggle to critically evaluate the ethical implications of their own decisions or those made by AI systems. This could result in a lack of awareness and the ability to make informed decisions based on ethical considerations.

The dependence on AI systems for information and problem-solving can also disrupt the development of critical thinking skills. Rather than critically analyzing information and evaluating different perspectives, students may rely solely on AI algorithms to provide them with answers. This reliance on AI can hinder the development of analytical and critical thinking skills that are important for understanding complex issues and making informed judgments.

Furthermore, an outdated curriculum can contribute to the loss of critical thinking skills in the age of AI. If educational institutions do not adapt their curricula to incorporate AI-related topics and challenges, students may not be adequately prepared to navigate the ethical and societal implications of AI. This can result in a lack of critical thinking skills when it comes to evaluating and responding to AI technology in various aspects of life.

Lastly, the increasing use of AI in education raises concerns about privacy and security. AI systems often collect and analyze large amounts of personal data, raising questions about the privacy and security of students’ information. This can affect students’ willingness to engage in critical thinking activities if they feel their privacy is being violated or if they are concerned about the potential misuse of their personal data.

Dependency on Technology

In the realm of education, there is an increasing dependence on technology, specifically AI, which poses several challenges.

Firstly, this dependency raises concerns about privacy. As AI gathers and analyzes large amounts of data, there is a risk of sensitive information being accessed or exploited. Students’ personal information could be vulnerable to hacking or misuse. Maintaining strict protocols and ensuring secure systems becomes crucial in protecting student data.

Furthermore, the reliance on AI can exacerbate existing inequalities. Access to technology and AI tools may not be evenly distributed, creating a divide between students who have the necessary resources and those who do not. This can widen the educational gap between different socio-economic groups, perpetuating inequalities in learning opportunities.

Another significant issue is the potential disruption caused by AI. As education systems incorporate AI-driven platforms and tools, traditional teaching methods may become obsolete. This can lead to job losses for educators who are replaced by automated systems. Additionally, students may rely too heavily on AI for learning, impacting their critical thinking and problem-solving abilities.

AI also brings the risk of bias. Algorithms used in AI systems can inadvertently perpetuate stereotypes and discrimination. If the AI systems are trained on biased or incomplete data, this bias can be reflected in the educational content. This can hinder the goal of providing unbiased and inclusive education to all students.

Moreover, the automation of tasks through AI can lead to a lack of emphasis on ethical considerations. AI systems are not inherently ethical, and their decisions can be based solely on data and algorithms. This can neglect the importance of human judgment and ethics in education, potentially leading to consequences that are ethically questionable or harmful.

Lastly, reliance on technology can expose the limitations of an outdated curriculum. AI advancements often outpace curriculum updates, making it difficult for educational institutions to keep up. As a result, students may not receive the necessary education and skills to adapt to the changing technological landscape.

In conclusion, while AI has the potential to revolutionize education, the increasing dependency on technology raises several concerns. Privacy, inequality, disruption, bias, automation, ethics, and outdated curriculum are all key challenges that must be carefully addressed to mitigate the negative impact of AI on education.

Impersonal Learning Experience

One of the negative impacts of AI on education is the potential for an impersonal learning experience. As AI technology becomes more prevalent in classrooms, students may find themselves losing the personal connection and individualized attention that is often found in traditional teaching methods. This can have several detrimental effects.

Privacy Concerns

With the use of AI in education, there is a potential invasion of privacy. AI systems often collect and analyze data on students, including their learning behaviors and personal information. This raises concerns about data security and the potential misuse of student data. Without proper safeguards in place, students may be at risk of having their personal information compromised.

Bias and Inequality

AI algorithms may perpetuate biases and inequalities in education, leading to an unfair learning experience for certain students. If the AI systems are trained on biased data or programmed with biased algorithms, they can unintentionally favor certain groups of students over others. This can further exacerbate existing inequalities and create a disadvantage for marginalized students.

Additionally, AI systems may not be able to adequately address the unique learning needs of individual students. They may rely on generalized approaches that do not consider specific learning styles or disabilities, resulting in a one-size-fits-all approach that can hinder the educational progress of some students.

Dependence and Automation

As AI takes over certain teaching tasks, there is a risk of students becoming too reliant on technology and losing important skills and knowledge. This dependence on AI for learning can hinder critical thinking, problem-solving, and creativity, as students rely on automated systems to provide answers and guidance.

Furthermore, the automation of educational processes can lead to a devaluation of human expertise in the teaching profession. The role of the teacher may become marginalized, leading to job loss and a disruption in the traditional education system.

Ethical Considerations

The implementation of AI in education raises important ethical considerations. For example, there may be concerns regarding the transparency and accountability of AI systems. Students and teachers may not fully understand how AI algorithms make decisions, leading to a lack of trust and potential ethical dilemmas.

Additionally, the use of AI may raise questions about the fairness of high-stakes assessments and grading. If AI systems are responsible for grading students’ work, there may be concerns about the accuracy and consistency of the grading process.

In conclusion, while AI has the potential to enhance education, it is important to address the negative impacts it can have on the learning experience. Measures should be taken to ensure privacy, prevent bias and inequality, maintain a balance between automation and human involvement, and address ethical considerations to create a more inclusive and effective use of AI in education.

Lack of Emotional Support

One of the negative impacts of AI on education is the lack of emotional support that students may encounter. While AI technology can provide personalized learning experiences and assist in academic tasks, it often falls short in providing emotional guidance and support.

Traditional educational settings have always emphasized the importance of emotional well-being and the role of teachers in providing guidance and support to students. However, with the increasing use of AI in education, there is a risk of neglecting the emotional aspect of learning.

An outdated curriculum that focuses solely on academic achievements and neglects the emotional development of students can be reinforced by AI-powered systems. These systems may prioritize test scores and academic performance over the emotional well-being of students, leading to a lack of support in dealing with emotions such as stress, anxiety, or frustration.

Ethics and Job Loss

Moreover, as AI technology becomes more advanced and capable of performing tasks traditionally done by humans, there is a concern about the loss of human interaction and the impact on emotional support.

Human teachers are not only responsible for delivering knowledge but also play a crucial role in providing emotional support, understanding individual needs, and creating a supportive classroom environment. The dependence on AI systems can lead to a reduction in human teachers’ roles, resulting in a potential loss of emotional support for students.

Dependence, Inequality, Bias, Privacy, and Disruption

Furthermore, AI-powered systems may also perpetuate inequality and bias in education. These systems rely on data to make personalized recommendations or decisions, but if the data used to train AI models are biased or incomplete, it can lead to discriminatory outcomes. Students from marginalized communities or with unique learning needs may not receive the necessary emotional support if the AI systems are not designed to address their specific needs.

Privacy is another concern when it comes to AI in education. The collection and analysis of student data can raise privacy issues, as sensitive information about students’ emotions and behaviors can be potentially accessed and misused. It is crucial to ensure that proper measures are in place to protect students’ privacy and maintain ethical standards.

In conclusion, while AI technology in education has the potential to revolutionize teaching and learning, it is essential to recognize the limitations and potential negative impacts it can have, such as the lack of emotional support. Balancing the benefits of AI with the need for emotional guidance and support is crucial to ensure the overall well-being and success of students.

The Future Impact on Students

In the future, the increasing integration of AI in education may bring both opportunities and challenges for students.

Disruption and Automation

One of the potential impacts of AI on students is the disruption caused by automation. AI technologies, such as chatbots and automated grading systems, have the potential to replace certain tasks traditionally performed by teachers, such as answering basic questions or grading assignments. While this automation may free up teachers’ time to focus on more complex tasks, it could also lead to a loss of the personal connection and guidance that students receive from human educators.

Another concern is the potential job loss for students. As AI continues to advance and take on more tasks, there is a possibility that certain jobs may become obsolete. This could impact students who are preparing for careers that are at risk of automation, leading to increased competition for a limited number of positions. It may be necessary for students to adapt and acquire skills that are less susceptible to automation in order to secure employment in the future.

Bias and Privacy

AI algorithms are not immune to bias, and this could have a negative impact on students. If AI systems are trained on biased data, they may perpetuate existing inequalities and stereotypes. Additionally, the use of AI in education raises concerns about privacy. Students’ personal information, such as their performance data and behavior patterns, may be collected and analyzed by AI systems, potentially compromising their privacy rights.

Outdated Curriculum

While AI can provide personalized learning experiences, there is also a risk that it may reinforce an outdated curriculum. If AI systems are programmed to prioritize certain topics or perspectives, students may not receive a well-rounded education. It is important to ensure that AI is used as a tool to enhance learning and critical thinking, rather than limiting students’ exposure to diverse ideas and knowledge.

As AI becomes more prevalent in education, ethical considerations should be at the forefront. Teachers, policymakers, and educational institutions must ensure that AI is used in an ethical and responsible manner. This includes addressing issues such as algorithmic transparency, accountability, and the potential for AI to exacerbate existing inequalities in access to education.

Addressing Inequality

AI has the potential to either perpetuate or address educational inequalities. While AI can provide access to quality education for students in remote or underserved areas, there is a risk that it may widen the existing gap between privileged and marginalized students. It is crucial to implement policies and initiatives that ensure equal access to AI-powered educational resources and opportunities for all students.

In conclusion, the future impact of AI on students in the field of education is both promising and challenging. It is important to recognize and address potential negative consequences, such as disruption, job loss, bias, privacy concerns, outdated curriculum, ethical considerations, and inequality, in order to maximize the benefits and ensure a fair and inclusive education system for all students.

Unprepared for a Changing World

As artificial intelligence (AI) continues to advance, it is clear that our education system is unprepared for the changes it brings. One major issue is an outdated curriculum that fails to address the skills needed in an AI-driven world.

The rapid progress of AI technology has caused disruption in various industries, and education is no exception. Many traditional teaching methods and subjects are becoming obsolete, leaving students ill-equipped to thrive in this new era.

Bias is another concern when it comes to AI in education. Without careful consideration and oversight, AI systems can perpetuate and even amplify existing inequalities. This can lead to a lack of access to quality education and opportunities for certain groups of students.

Furthermore, the ethical implications of AI in education must be carefully examined. AI algorithms can make decisions that influence students’ educational paths, such as recommending courses or career paths. If these algorithms are biased or flawed, it can have long-lasting negative effects on students’ future prospects.

Additionally, there is a growing dependence on AI systems in education. While technology can enhance the learning experience, reliance on automation can diminish critical thinking and problem-solving skills. Students may become overly reliant on AI tools, impacting their ability to think independently and creatively.

Job loss is also a concern as AI continues to automate tasks traditionally performed by humans. Teachers may find themselves replaced by AI-powered platforms for certain subjects or administrative tasks. This can lead to a decrease in job opportunities and further inequalities within the education sector.

In conclusion, the negative impact of AI on education is evident in various aspects such as an outdated curriculum, disruption, bias, inequality, ethics, dependence, and potential job loss. It is crucial for educators and policymakers to proactively address these challenges and ensure that our education system prepares students for a rapidly changing world driven by AI technology.

Emotional Development Issues

The integration of AI in education brings forth a number of emotional development issues that need to be considered. While AI offers various benefits, its use can also have negative impacts on students’ emotional well-being.

One concern is the issue of privacy. With AI-powered educational systems, there is a potential risk of personal data being collected and stored without the knowledge or consent of students. This can lead to the violation of privacy rights, causing emotional distress and a sense of insecurity among students.

Another issue is the ethical implications of using AI in education. AI systems can make decisions or judgments based on algorithms that may be biased or unfair. This can create a sense of injustice among students and affect their emotional development. It is crucial to ensure that AI systems are designed and used ethically to avoid such negative impacts.

Additionally, the disruption caused by the introduction of AI in education can also have emotional consequences for students. Automation of certain tasks may lead to a sense of detachment and lack of personal interaction, which is essential for emotional growth and development.

Furthermore, excessive dependence on AI for learning can hinder students’ ability to think critically and solve problems independently. This can lead to feelings of inadequacy and dependence, negatively impacting their emotional well-being.

Bias is another significant emotional development issue associated with AI in education. AI systems are often trained on existing data, which may contain biases or stereotypes. This can perpetuate discrimination and exclusion, causing emotional distress and affecting students’ self-esteem.

Moreover, the use of AI in education may expose students to outdated curriculum and resources. AI systems may not be able to adapt and update educational content fast enough, resulting in a lack of relevance and effectiveness. This can leave students feeling disengaged and frustrated, hindering their emotional well-being.

Finally, the potential job loss due to the automation of certain tasks can have a profound impact on students’ emotional development. Uncertainty and fear about future employment prospects can create anxiety and stress, affecting their overall well-being.

Overall, while AI has the potential to improve education, it is crucial to address the emotional development issues associated with its use. Privacy concerns, ethical implications, disruption, automation, dependence, bias, outdated curriculum, and job loss are important factors that need to be carefully considered and managed to ensure a positive impact on students’ emotional well-being.

Reduced Creativity and Innovation

While AI has the potential to greatly enhance educational experiences, it also poses a threat to creativity and innovation in the classroom. AI systems are designed to follow predetermined algorithms, which may value efficiency and productivity over creative thinking and problem-solving. This can result in a curriculum that focuses only on teaching students how to replicate the same solutions and ideas, rather than encouraging them to think critically and come up with their own unique solutions.

The use of AI in education can also contribute to a lack of diversity and inclusiveness. AI algorithms are developed by humans and can inadvertently perpetuate biases and inequalities. When AI is used to evaluate student performance or recommend educational pathways, it may not take into account individual circumstances, abilities, or different learning styles, potentially leading to unequal educational opportunities and outcomes.

Another potential negative impact of AI in education is the potential for job loss among educators. While AI can automate repetitive administrative tasks, such as grading papers or managing student records, it cannot replace the human connection and personalized guidance that teachers provide. Dependence on AI systems could lead to a decrease in the number of teaching positions, resulting in less individualized attention for students and a loss of human interaction that is crucial for student development.

Furthermore, the rapid advancement of AI technology can lead to an outdated curriculum. As AI continues to evolve, the skillsets and knowledge required for students to thrive in the future workforce may change as well. However, educational institutions may struggle to keep up with these changes, leading to a curriculum that does not adequately prepare students for the careers and challenges of the future.

Ethical concerns also arise when AI is integrated into education. There are questions about the privacy and security of student data collected by AI systems, as well as the potential for misuse or abuse of this data. Additionally, AI algorithms may lack transparency, making it difficult for students and educators to understand how decisions are being made and to trust the recommendations provided by AI systems.

The disruption caused by AI in education can also have unintended consequences. Students who rely heavily on AI systems for their educational needs may become less self-reliant and less capable of independent learning. They may become dependent on AI systems to provide answers and solutions, rather than developing critical thinking and problem-solving skills on their own.

In conclusion, while AI has the potential to enhance education in many ways, caution must be exercised to ensure that it does not lead to reduced creativity and innovation in the classroom. Measures should be taken to address the potential inequality, bias, job loss, dependence, outdated curriculum, ethics, and disruption that can arise from the integration of AI into education.

Question-answer:

What are the negative impacts of ai on education.

AI can have negative impacts on education in several ways. It can lead to job displacement for educators as AI can take over certain tasks such as grading papers or providing personalized feedback. Additionally, AI may not be able to replicate the human connection and emotional support that educators provide, which can be detrimental to the learning experience. Moreover, there are concerns about data privacy and security as AI collects and analyzes vast amounts of student data.

How does AI affect the inequality in education?

AI can exacerbate educational inequality. It requires access to technology and internet connectivity, which not all students have. This can create a digital divide, where students from disadvantaged backgrounds are left behind. Moreover, AI can perpetuate biases and discrimination if the algorithms are not designed properly. For example, if an AI grading system is trained on data with inherent biases, it may perpetuate those biases in its evaluations.

Can AI completely replace teachers in the future?

While AI can automate certain tasks, it is unlikely to completely replace teachers in the future. Teachers play a crucial role in providing personalized guidance, emotional support, and fostering critical thinking skills in students. AI can assist and enhance the educational experience, but human interaction and empathy are still essential for effective education.

What measures can be taken to mitigate the negative impact of AI on education?

To mitigate the negative impact of AI on education, several measures can be taken. First, schools and policymakers need to ensure equal access to technology and bridge the digital divide. Second, there should be transparency and accountability in AI algorithms to avoid perpetuating biases. Educators can also receive training and professional development to effectively incorporate AI into their teaching practices. Lastly, privacy and security concerns related to student data should be addressed through proper regulations and safeguards.

Are there any positive impacts of AI on education?

Yes, there are positive impacts of AI on education. AI can provide personalized learning experiences, adaptive content, and instant feedback to students, which can enhance their learning outcomes. It can also automate administrative tasks, allowing educators to focus more on individualized instruction. Furthermore, AI can analyze large amounts of data to identify patterns and trends, which can inform educational policies and improve overall education systems.

What is the negative impact of AI on education?

The negative impact of AI on education includes job displacement for teachers, lack of personalized teaching, and the potential for biases in the algorithms used in AI systems.

What are the negative impacts of Artificial Intelligence on students?

Artificial Intelligence can have negative effects on students, such as reduced human interaction, over-reliance on technology, and potential threats to privacy and security.

How does AI integration affect student learning and teaching?

Integrating AI in education can enhance personalized learning experiences, but it may also lead to decreased social skills and a less holistic approach to education.

What are the challenges of AI adoption in the education sector?

Challenges of adopting AI in education include the cost of implementation, lack of teacher training, and concerns regarding data privacy and ethical use of AI technologies.

What are the risks associated with using Artificial Intelligence in education?

Risks of AI in education include potential biases in algorithms, data security breaches, and the displacement of human educators by technology.

How can the negative impacts of AI on students be minimized?

To minimize the negative impact of AI on students, it is important to provide proper training for teachers and students, ensure transparency in AI systems, and maintain a balance between technology and traditional teaching methods.

What are the disadvantages of AI integration in the education sector?

Disadvantages of AI in education include the risk of job loss for educators, over-reliance on technology, and challenges in ensuring equitable access to AI tools for all students.

Are there any benefits of Artificial Intelligence in education?

Yes, AI can enhance personalized learning, provide real-time feedback to students, improve administrative processes, and offer new learning opportunities through adaptive technologies.

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Positive and Negative Impacts of Artificial Intelligence

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How to solve AI’s inequality problem

New digital technologies are exacerbating inequality. Here’s how scientists creating AI can make better choices.

• David Rotman archive page

The economy is being transformed by digital technologies, especially in artificial intelligence, that are rapidly changing how we live and work. But this transformation poses a troubling puzzle: these technologies haven’t done much to grow the economy, even as income inequality worsens. Productivity growth, which economists consider essential to improving living standards, has largely been sluggish since at least the mid-2000s in many countries. 

Why are these technologies failing to produce more economic growth? Why aren’t they fueling more widespread prosperity? To get at an answer, some leading economists and policy experts are looking more closely at how we invent and deploy AI and automation—and identifying ways we can make better choices.  

In an essay called “ The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence ,” Erik Brynjolfsson, director of the Stanford Digital Economy Lab, writes of the way AI researchers and businesses have focused on building machines to replicate human intelligence. The title, of course, is a reference to Alan Turing and his famous 1950 test for whether a machine is intelligent: Can it imitate a person so well that you can’t tell it isn’t one? Ever since then, says Brynjolfsson, many researchers have been chasing this goal. But, he says, the obsession with mimicking human intelligence has led to AI and automation that too often simply replace workers, rather than extending human capabilities and allowing people to do new tasks. 

For Brynjolfsson, an economist, simple automation, while producing value, can also be a path to greater inequality of income and wealth. The excessive focus on human-like AI, he writes, drives down wages for most people “even as it amplifies the market power of a few” who own and control the technologies. The emphasis on automation rather than augmentation is, he argues in the essay, the “single biggest explanation” for the rise of billionaires at a time when average real wages for many Americans have fallen. 

Brynjolfsson is no Luddite. His 2014 book, coauthored with Andrew McAfee, is called The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies . But he says the thinking of AI researchers has been too limited. “I talk to many researchers, and they say: ‘Our job is to make a machine that is like a human.’ It’s a clear vision,” he says. But, he adds, “it’s also kind of a lazy, low bar.’” 

In the long run, he argues, far more value is created by using AI to produce new goods and services, rather than simply trying to replace workers. But he says that for businesses, driven by a desire to cut costs, it’s often easier to just swap in a machine than to rethink processes and invest in technologies that take advantage of AI to expand the company’s products and improve the productivity of its workers. 

Recent advances in AI have been impressive, leading to everything from driverless cars to human-like language models. Guiding the trajectory of the technology is critical, however. Because of the choices that researchers and businesses have made so far, new digital technologies have created vast wealth for those owning and inventing them, while too often destroying opportunities for those in jobs vulnerable to being replaced. These inventions have generated good tech jobs in a handful of cities, like San Francisco and Seattle, while much of the rest of the population has been left behind. But it doesn’t have to be that way. 

Daron Acemoglu, an MIT economist, provides compelling evidence for the role automation, robots, and algorithms that replace tasks done by human workers have played in slowing wage growth and worsening inequality in the US. In fact, he says, 50 to 70% of the growth in US wage inequality between 1980 and 2016 was caused by automation.

That’s mostly before the surge in the use of AI technologies. And Acemoglu worries that AI-based automation will make matters even worse. Early in the 20th century and during previous periods, shifts in technology typically produced more good new jobs than they destroyed, but that no longer seems to be the case. One reason is that companies are often choosing to deploy what he and his collaborator Pascual Restrepo call “so-so technologies,” which replace workers but do little to improve productivity or create new business opportunities. 

At the same time, businesses and researchers are largely ignoring the potential of AI technologies to expand the capabilities of workers while delivering better services. Acemoglu points to digital technologies that could allow nurses to diagnose illnesses more accurately or help teachers provide more personalized lessons to students.  

Government, AI scientists, and Big Tech are all guilty of making decisions that favor excessive automation, says Acemoglu. Federal tax policies favor machines. While human labor is heavily taxed, there is no payroll tax on robots or automation. And, he says, AI researchers have “no compunction [about] working on technologies that automate work at the expense of lots of people losing their jobs.” 

But he reserves his strongest ire for Big Tech, citing data indicating that US and Chinese tech giants fund roughly two-thirds of AI work. “I don’t think it’s an accident that we have so much emphasis on automation when the future of technology in this country is in the hands of a few companies like Google, Amazon, Facebook, Microsoft, and so on that have algorithmic automation as their business model,” he says.

Anger over AI’s role in exacerbating inequality could endanger the technology’s future. In her new book Cogs and Monsters: What Economics Is, and What It Should Be , Diane Coyle, an economist at Cambridge University, argues that the digital economy requires new ways of thinking about progress. “Whatever we mean by the economy growing, by things getting better, the gains will have to be more evenly shared than in the recent past,” she writes. “An economy of tech millionaires or billionaires and gig workers, with middle-income jobs undercut by automation, will not be politically sustainable.” 

Improving living standards and increasing prosperity for more people will require greater use of digital technologies to boost productivity in various sectors, including health care and construction, says Coyle. But people can’t be expected to embrace the changes if they’re not seeing the benefits—if they’re just seeing good jobs being destroyed.

In a recent interview with MIT Technology Review, Coyle said she fears that tech’s inequality problem could be a roadblock to deploying AI. “We’re talking about disruption,” she says. “These are transformative technologies that change the ways we spend our time every day, that change business models that succeed.” To make such “tremendous changes,” she adds, you need social buy-in.

Instead, says Coyle, resentment is simmering among many as the benefits are perceived to go to elites in a handful of prosperous cities. 

In the US, for instance, during much of the 20th century the various regions of the country were—in the language of economists—“converging,” and financial disparities decreased. Then, in the 1980s, came the onslaught of digital technologies, and the trend reversed itself. Automation wiped out many manufacturing and retail jobs. New, well-paying tech jobs were clustered in a few cities.

According to the Brookings Institution, a short list of eight American cities that included San Francisco, San Jose, Boston, and Seattle had roughly 38% of all tech jobs by 2019. New AI technologies are particularly concentrated: Brookings’s Mark Muro and Sifan Liu estimate that just 15 cities account for two-thirds of the AI assets and capabilities in the United States (San Francisco and San Jose alone account for about one-quarter).

The dominance of a few cities in the invention and commercialization of AI means that geographical disparities in wealth will continue to soar. Not only will this foster political and social unrest, but it could, as Coyle suggests, hold back the sorts of AI technologies needed for regional economies to grow. 

Part of the solution could lie in somehow loosening the stranglehold that Big Tech has on defining the AI agenda. That will likely take increased federal funding for research independent of the tech giants. Muro and others have suggested hefty federal funding to help create US regional innovation centers , for example. 

A more immediate response is to broaden our digital imaginations to conceive of AI technologies that don’t simply replace jobs but expand opportunities in the sectors that different parts of the country care most about, like health care, education, and manufacturing. 

Changing minds

The fondnesss that AI and robotics researchers have for replicating the capabilities of humans often means trying to get a machine to do a task that’s easy for people but daunting for the technology. Making a bed, for example, or an espresso. Or driving a car. Seeing an autonomous car navigate a city’s street or a robot act as a barista is amazing. But too often, the people who develop and deploy these technologies don’t give much thought to the potential impact on jobs and labor markets.  

Anton Korinek, an economist at the University of Virginia and a Rubenstein Fellow at Brookings, says the tens of billions of dollars that have gone into building autonomous cars will inevitably have a negative effect on labor markets once such vehicles are deployed, taking the jobs of countless drivers. What if, he asks, those billions had been invested in AI tools that would be more likely to expand labor opportunities? 

When applying for funding at places like the US National Science Foundation and the National Institutes of Health, Korinek explains, “no one asks, ‘How will it affect labor markets?’”

Katya Klinova, a policy expert at the Partnership on AI in San Francisco , is working on ways to get AI scientists to rethink the ways they measure success. “When you look at AI research, and you look at the benchmarks that are used pretty much universally, they’re all tied to matching or comparing to human performance,” she says. That is, AI scientists grade their programs in, say, image recognition against how well a person can identify an object. 

Such benchmarks have driven the direction of the research, Klinova says. “It’s no surprise that what has come out is automation and more powerful automation,” she adds. “Benchmarks are super important to AI developers—especially for young scientists, who are entering en masse into AI and asking, ‘What should I work on?’” 

But benchmarks for the performance of human-machine collaborations are lacking, says Klinova, though she has begun working to help create some. Collaborating with Korinek, she and her team at Partnership for AI are also writing a user guide for AI developers who have no background in economics to help them understand how workers might be affected by the research they are doing. 

“It’s about changing the narrative away from one where AI innovators are given a blank ticket to disrupt and then it’s up to the society and government to deal with it,” says Klinova. Every AI firm has some kind of answer about AI bias and ethics, she says, “but they’re still not there for labor impacts.”

The pandemic has accelerated the digital transition. Businesses have understandably turned to automation to replace workers. But the pandemic has also pointed to the potential of digital technologies to expand our abilities. They’ve given us research tools to help create new vaccines and provided a viable way for many to work from home. 

As AI inevitably expands its impact, it will be worth watching to see whether this leads to even greater damage to good jobs—and more inequality. “I’m optimistic we can steer the technology in the right way,” says Brynjolfsson. But, he adds, that will mean making deliberate choices about the technologies we create and invest in.

“The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence” Erik Brynjolfsson Daedalus, Spring 2022

“The wrong kind of AI? Artificial intelligence and the future of labour demand” Daron Acemoglu and Pascual Restrepo Cambridge Journal Of Regions, Economy and Society, March 2020

Artificial intelligence

Large language models can do jaw-dropping things. but nobody knows exactly why..

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

• Will Douglas Heaven archive page

OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

Google’s Gemini is now in everything. Here’s how you can try it out.

Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

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Experience Stern | Faculty & Research

The ai effect: how artificial intelligence is shaping the economy.

Overview : In the essay “ The Business Revolution: Economy-Wide Impacts of Artificial Intelligence and Digital Platforms ,” NYU Stern Professor Hanna Halaburda , with co-authors Jeffrey Prince (Indiana University), D. Daniel Sokol (USC Marshall), and Feng Zhu (Harvard University), explore themes around digital business transformation and review the impact of artificial intelligence (AI) and digital platforms on specific aspects of the economy: pricing, healthcare, and content.

Why study this now : During the past three decades, business has been transformed from its leveraging of technological advances. The application of this technology has shifted organization value from tangible goods to intangible assets, and from production happening within a company to a dynamic where third parties are instead creating much of the value. The authors of this essay call attention to the overarching effects of AI on business as well as highlight more specific impacts of AI and digital platforms within certain industries.

What the authors spotlight : While AI has the potential to change the way companies work (e.g., increase labor productivity, improve decision-making), its potential for harm should not be overlooked. The authors also shine light on some of the positive and negative effects of AI and digital platforms on certain elements of the economy:

• Pricing : AI pricing algorithms can increase the ability for dynamic pricing, but also may learn to collude over time (even if not explicitly programmed to).
• Healthcare : AI may improve health outcomes for certain diagnoses (e.g., cancer detection, heart disease), but trust in the technology is a major consideration from both the patient and staff sides in terms of adoption.
• Content : Digital technologies have disrupted aspects of industries like music, movies, and books (e.g., reduced costs of distribution, increased differentiation), but challenges like piracy and antitrust concerns exist. 

Furthermore, the authors discuss the potential for mergers and acquisitions to help companies navigate the numerous changes and challenges that digital technology brings.

What does this  change : The authors note that we are in the early stages of the digital business revolution, and that there is growing interest in using empirical research to study digital platforms. They also see an increase in transformations from B2B industries.

Key insight : “The digital business revolution has made existing routines more efficient,” say the authors, “and it has created opportunities to rethink how firms and the economy are organized.”

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Artificial intelligence in education: Addressing ethical challenges in K-12 settings

Selin akgun.

Michigan State University, East Lansing, MI USA

Christine Greenhow

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Not applicable.

Artificial intelligence (AI) is a field of study that combines the applications of machine learning, algorithm productions, and natural language processing. Applications of AI transform the tools of education. AI has a variety of educational applications, such as personalized learning platforms to promote students’ learning, automated assessment systems to aid teachers, and facial recognition systems to generate insights about learners’ behaviors. Despite the potential benefits of AI to support students’ learning experiences and teachers’ practices, the ethical and societal drawbacks of these systems are rarely fully considered in K-12 educational contexts. The ethical challenges of AI in education must be identified and introduced to teachers and students. To address these issues, this paper (1) briefly defines AI through the concepts of machine learning and algorithms; (2) introduces applications of AI in educational settings and benefits of AI systems to support students’ learning processes; (3) describes ethical challenges and dilemmas of using AI in education; and (4) addresses the teaching and understanding of AI by providing recommended instructional resources from two providers—i.e., the Massachusetts Institute of Technology’s (MIT) Media Lab and Code.org. The article aims to help practitioners reap the benefits and navigate ethical challenges of integrating AI in K-12 classrooms, while also introducing instructional resources that teachers can use to advance K-12 students’ understanding of AI and ethics.

Introduction

“Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks.” — Stephen Hawking.

We may not think about artificial intelligence (AI) on a daily basis, but it is all around us, and we have been using it for years. When we are doing a Google search, reading our emails, getting a doctor’s appointment, asking for driving directions, or getting movie and music recommendations, we are constantly using the applications of AI and its assistance in our lives. This need for assistance and our dependence on AI systems has become even more apparent during the COVID-19 pandemic. The growing impact and dominance of AI systems reveals itself in healthcare, education, communications, transportation, agriculture, and more. It is almost impossible to live in a modern society without encountering applications powered by AI  [ 10 , 32 ].

Artificial intelligence (AI) can be defined briefly as the branch of computer science that deals with the simulation of intelligent behavior in computers and their capacity to mimic, and ideally improve, human behavior [ 43 ]. AI dominates the fields of science, engineering, and technology, but also is present in education through machine-learning systems and algorithm productions [ 43 ]. For instance, AI has a variety of algorithmic applications in education, such as personalized learning systems to promote students’ learning, automated assessment systems to support teachers in evaluating what students know, and facial recognition systems to provide insights about learners’ behaviors [ 49 ]. Besides these platforms, algorithm systems are prominent in education through different social media outlets, such as social network sites, microblogging systems, and mobile applications. Social media are increasingly integrated into K-12 education [ 7 ] and subordinate learners’ activities to intelligent algorithm systems [ 17 ]. Here, we use the American term “K–12 education” to refer to students’ education in kindergarten (K) (ages 5–6) through 12th grade (ages 17–18) in the United States, which is similar to primary and secondary education or pre-college level schooling in other countries. These AI systems can increase the capacity of K-12 educational systems and support the social and cognitive development of students and teachers [ 55 , 8 ]. More specifically, applications of AI can support instruction in mixed-ability classrooms; while personalized learning systems provide students with detailed and timely feedback about their writing products, automated assessment systems support teachers by freeing them from excessive workloads [ 26 , 42 ].

Despite the benefits of AI applications for education, they pose societal and ethical drawbacks. As the famous scientist, Stephen Hawking, pointed out that weighing these risks is vital for the future of humanity. Therefore, it is critical to take action toward addressing them. The biggest risks of integrating these algorithms in K-12 contexts are: (a) perpetuating existing systemic bias and discrimination, (b) perpetuating unfairness for students from mostly disadvantaged and marginalized groups, and (c) amplifying racism, sexism, xenophobia, and other forms of injustice and inequity [ 40 ]. These algorithms do not occur in a vacuum; rather, they shape and are shaped by ever-evolving cultural, social, institutional and political forces and structures [ 33 , 34 ]. As academics, scientists, and citizens, we have a responsibility to educate teachers and students to recognize the ethical challenges and implications of algorithm use. To create a future generation where an inclusive and diverse citizenry can participate in the development of the future of AI, we need to develop opportunities for K-12 students and teachers to learn about AI via AI- and ethics-based curricula and professional development [ 2 , 58 ]

Toward this end, the existing literature provides little guidance and contains a limited number of studies that focus on supporting K-12 students and teachers’ understanding of social, cultural, and ethical implications of AI [ 2 ]. Most studies reflect university students’ engagement with ethical ideas about algorithmic bias, but few addresses how to promote students’ understanding of AI and ethics in K-12 settings. Therefore, this article: (a) synthesizes ethical issues surrounding AI in education as identified in the educational literature, (b) reflects on different approaches and curriculum materials available for teaching students about AI and ethics (i.e., featuring materials from the MIT Media Lab and Code.org), and (c) articulates future directions for research and recommendations for practitioners seeking to navigate AI and ethics in K-12 settings.

Next, we briefly define the notion of artificial intelligence (AI) and its applications through machine-learning and algorithm systems. As educational and educational technology scholars working in the United States, and at the risk of oversimplifying, we provide only a brief definition of AI below, and recognize that definitions of AI are complex, multidimensional, and contested in the literature [ 9 , 16 , 38 ]; an in-depth discussion of these complexities, however, is beyond the scope of this paper. Second, we describe in more detail five applications of AI in education, outlining their potential benefits for educators and students. Third, we describe the ethical challenges they raise by posing the question: “how and in what ways do algorithms manipulate us?” Fourth, we explain how to support students’ learning about AI and ethics through different curriculum materials and teaching practices in K-12 settings. Our goal here is to provide strategies for practitioners to reap the benefits while navigating the ethical challenges. We acknowledge that in centering this work within U.S. education, we highlight certain ethical issues that educators in other parts of the world may see as less prominent. For example, the European Union (EU) has highlighted ethical concerns and implications of AI, emphasized privacy protection, surveillance, and non-discrimination as primary areas of interest, and provided guidelines on how trustworthy AI should be [ 3 , 15 , 23 ]. Finally, we reflect on future directions for educational and other research that could support K-12 teachers and students in reaping the benefits while mitigating the drawbacks of AI in education.

Definition and applications of artificial intelligence

The pursuit of creating intelligent machines that replicate human behavior has accelerated with the realization of artificial intelligence. With the latest advancements in computer science, a proliferation of definitions and explanations of what counts as AI systems has emerged. For instance, AI has been defined as “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings” [ 49 ]. This particular definition highlights the mimicry of human behavior and consciousness. Furthermore, AI has been defined as “the combination of cognitive automation, machine learning, reasoning, hypothesis generation and analysis, natural language processing, and intentional algorithm mutation producing insights and analytics at or above human capability” [ 31 ]. This definition incorporates the different sub-fields of AI together and underlines their function while reaching at or above human capability.

Combining these definitions, artificial intelligence can be described as the technology that builds systems to think and act like humans with the ability of achieving goals . AI is mainly known through different applications and advanced computer programs, such as recommender systems (e.g., YouTube, Netflix), personal assistants (e.g., Apple’s Siri), facial recognition systems (e.g., Facebook’s face detection in photographs), and learning apps (e.g., Duolingo) [ 32 ]. To build on these programs, different sub-fields of AI have been used in a diverse range of applications. Evolutionary algorithms and machine learning are most relevant to AI in K-12 education.

Algorithms are the core elements of AI. The history of AI is closely connected to the development of sophisticated and evolutionary algorithms. An algorithm is a set of rules or instructions that is to be followed by computers in problem-solving operations to achieve an intended end goal. In essence, all computer programs are algorithms. They involve thousands of lines of codes which represent mathematical instructions that the computer follows to solve the intended problems (e.g., as computing numerical calculation, processing an image, and grammar-checking in an essay). AI algorithms are applied to fields that we might think of as essentially human behavior—such as speech and face recognition, visual perception, learning, and decision-making and learning. In that way, algorithms can provide instructions for almost any AI system and application we can conceive [ 27 ].

Machine learning

Machine learning is derived from statistical learning methods and uses data and algorithms to perform tasks which are typically performed by humans [ 43 ]. Machine learning is about making computers act or perform without being given any line-by-line step [ 29 ]. The working mechanism of machine learning is the learning model’s exposure to ample amounts of quality data [ 41 ]. Machine-learning algorithms first analyze the data to determine patterns and to build a model and then predict future values through these models. In other words, machine learning can be considered a three-step process. First, it analyzes and gathers the data, and then, it builds a model to excel for different tasks, and finally, it undertakes the action and produces the desired results successfully without human intervention [ 29 , 56 ]. The widely known AI applications such as recommender or facial recognition systems have all been made possible through the working principles of machine learning.

Benefits of AI applications in education

Personalized learning systems, automated assessments, facial recognition systems, chatbots (social media sites), and predictive analytics tools are being deployed increasingly in K-12 educational settings; they are powered by machine-learning systems and algorithms [ 29 ]. These applications of AI have shown promise to support teachers and students in various ways: (a) providing instruction in mixed-ability classrooms, (b) providing students with detailed and timely feedback on their writing products, (c) freeing teachers from the burden of possessing all knowledge and giving them more room to support their students while they are observing, discussing, and gathering information in their collaborative knowledge-building processes [ 26 , 50 ]. Below, we outline benefits of each of these educational applications in the K-12 setting before turning to a synthesis of their ethical challenges and drawbacks.

Personalized learning systems

Personalized learning systems, also known as adaptive learning platforms or intelligent tutoring systems, are one of the most common and valuable applications of AI to support students and teachers. They provide students access to different learning materials based on their individual learning needs and subjects [ 55 ]. For example, rather than practicing chemistry on a worksheet or reading a textbook, students may use an adaptive and interactive multimedia version of the course content [ 39 ]. Comparing students’ scores on researcher-developed or standardized tests, research shows that the instruction based on personalized learning systems resulted in higher test scores than traditional teacher-led instruction [ 36 ]. Microsoft’s recent report (2018) of over 2000 students and teachers from Singapore, the U.S., the UK, and Canada shows that AI supports students’ learning progressions. These platforms promise to identify gaps in students’ prior knowledge by accommodating learning tools and materials to support students’ growth. These systems generate models of learners using their knowledge and cognition; however, the existing platforms do not yet provide models for learners’ social, emotional, and motivational states [ 28 ]. Considering the shift to remote K-12 education during the COVID-19 pandemic, personalized learning systems offer a promising form of distance learning that could reshape K-12 instruction for the future [ 35 ].

Automated assessment systems

Automated assessment systems are becoming one of the most prominent and promising applications of machine learning in K-12 education [ 42 ]. These scoring algorithm systems are being developed to meet the need for scoring students’ writing, exams and assignments, and tasks usually performed by the teacher. Assessment algorithms can provide course support and management tools to lessen teachers’ workload, as well as extend their capacity and productivity. Ideally, these systems can provide levels of support to students, as their essays can be graded quickly [ 55 ]. Providers of the biggest open online courses such as Coursera and EdX have integrated automated scoring engines into their learning platforms to assess the writings of hundreds of students [ 42 ]. On the other hand, a tool called “Gradescope” has been used by over 500 universities to develop and streamline scoring and assessment [ 12 ]. By flagging the wrong answers and marking the correct ones, the tool supports instructors by eliminating their manual grading time and effort. Thus, automated assessment systems deal very differently with marking and giving feedback to essays compared to numeric assessments which analyze right or wrong answers on the test. Overall, these scoring systems have the potential to deal with the complexities of the teaching context and support students’ learning process by providing them with feedback and guidance to improve and revise their writing.

Facial recognition systems and predictive analytics

Facial recognition software is used to capture and monitor students’ facial expressions. These systems provide insights about students’ behaviors during learning processes and allow teachers to take action or intervene, which, in turn, helps teachers develop learner-centered practices and increase student’s engagement [ 55 ]. Predictive analytics algorithm systems are mainly used to identify and detect patterns about learners based on statistical analysis. For example, these analytics can be used to detect university students who are at risk of failing or not completing a course. Through these identifications, instructors can intervene and get students the help they need [ 55 ].

Social networking sites and chatbots

Social networking sites (SNSs) connect students and teachers through social media outlets. Researchers have emphasized the importance of using SNSs (such as Facebook) to expand learning opportunities beyond the classroom, monitor students’ well-being, and deepen student–teacher relations [ 5 ]. Different scholars have examined the role of social media in education, describing its impact on student and teacher learning and scholarly communication [ 6 ]. They point out that the integration of social media can foster students’ active learning, collaboration skills, and connections with communities beyond the classroom [ 6 ]. Chatbots also take place in social media outlets through different AI systems [ 21 ]. They are also known as dialogue systems or conversational agents [ 26 , 52 ]. Chatbots are helpful in terms of their ability to respond naturally with a conversational tone. For instance, a text-based chatbot system called “Pounce” was used at Georgia State University to help students through the registration and admission process, as well as financial aid and other administrative tasks [ 7 ].

In summary, applications of AI can positively impact students’ and teachers’ educational experiences and help them address instructional challenges and concerns. On the other hand, AI cannot be a substitute for human interaction [ 22 , 47 ]. Students have a wide range of learning styles and needs. Although AI can be a time-saving and cognitive aide for teachers, it is but one tool in the teachers’ toolkit. Therefore, it is critical for teachers and students to understand the limits, potential risks, and ethical drawbacks of AI applications in education if they are to reap the benefits of AI and minimize the costs [ 11 ].

Ethical concerns and potential risks of AI applications in education

The ethical challenges and risks posed by AI systems seemingly run counter to marketing efforts that present algorithms to the public as if they are objective and value-neutral tools. In essence, algorithms reflect the values of their builders who hold positions of power [ 26 ]. Whenever people create algorithms, they also create a set of data that represent society’s historical and systemic biases, which ultimately transform into algorithmic bias. Even though the bias is embedded into the algorithmic model with no explicit intention, we can see various gender and racial biases in different AI-based platforms [ 54 ].

Considering the different forms of bias and ethical challenges of AI applications in K-12 settings, we will focus on problems of privacy, surveillance, autonomy, bias, and discrimination (see Fig.  1 ). However, it is important to acknowledge that educators will have different ethical concerns and challenges depending on their students’ grade and age of development. Where strategies and resources are recommended, we indicate the age and/or grade level of student(s) they are targeting (Fig. ​ (Fig.2 2 ).

Potential ethical and societal risks of AI applications in education

Student work from the activity of “Youtube Redesign” (MIT Media Lab, AI and Ethics Curriculum, p.1, [ 45 ])

One of the biggest ethical issues surrounding the use of AI in K-12 education relates to the privacy concerns of students and teachers [ 47 , 49 , 54 ]. Privacy violations mainly occur as people expose an excessive amount of personal information in online platforms. Although existing legislation and standards exist to protect sensitive personal data, AI-based tech companies’ violations with respect to data access and security increase people’s privacy concerns [ 42 , 54 ]. To address these concerns, AI systems ask for users’ consent to access their personal data. Although consent requests are designed to be protective measures and to help alleviate privacy concerns, many individuals give their consent without knowing or considering the extent of the information (metadata) they are sharing, such as the language spoken, racial identity, biographical data, and location [ 49 ]. Such uninformed sharing in effect undermines human agency and privacy. In other words, people’s agency diminishes as AI systems reduce introspective and independent thought [ 55 ]. Relatedly, scholars have raised the ethical issue of forcing students and parents to use these algorithms as part of their education even if they explicitly agree to give up privacy [ 14 , 48 ]. They really have no choice if these systems are required by public schools.

Another ethical concern surrounding the use of AI in K-12 education is surveillance or tracking systems which gather detailed information about the actions and preferences of students and teachers. Through algorithms and machine-learning models, AI tracking systems not only necessitate monitoring of activities but also determine the future preferences and actions of their users [ 47 ]. Surveillance mechanisms can be embedded into AI’s predictive systems to foresee students’ learning performances, strengths, weaknesses, and learning patterns . For instance, research suggests that teachers who use social networking sites (SNSs) for pedagogical purposes encounter a number of problems, such as concerns in relation to boundaries of privacy, friendship authority, as well as responsibility and availability [ 5 ]. While monitoring and patrolling students’ actions might be considered part of a teacher’s responsibility and a pedagogical tool to intervene in dangerous online cases (such as cyber-bullying or exposure to sexual content), such actions can also be seen as surveillance systems which are problematic in terms of threatening students’ privacy. Monitoring and tracking students’ online conversations and actions also may limit their participation in the learning event and make them feel unsafe to take ownership for their ideas. How can students feel secure and safe, if they know that AI systems are used for surveilling and policing their thoughts and actions? [ 49 ].

Problems also emerge when surveillance systems trigger issues related to autonomy, more specifically, the person’s ability to act on her or his own interest and values. Predictive systems which are powered by algorithms jeopardize students and teachers’ autonomy and their ability to govern their own life [ 46 , 47 ]. Use of algorithms to make predictions about individuals’ actions based on their information raise questions about fairness and self-freedom [ 19 ]. Therefore, the risks of predictive analysis also include the perpetuation of existing bias and prejudices of social discrimination and stratification [ 42 ].

Finally, bias and discrimination are critical concerns in debates of AI ethics in K-12 education [ 6 ]. In AI platforms, the existing power structures and biases are embedded into machine-learning models [ 6 ]. Gender bias is one of the most apparent forms of this problem, as the bias is revealed when students in language learning courses use AI to translate between a gender-specific language and one that is less-so. For example, while Google Translate translated the Turkish equivalent of “S he/he is a nurse ” into the feminine form, it also translated the Turkish equivalent of “ She/he is a doctor ” into the masculine form [ 33 ]. This shows how AI models in language translation carry the societal biases and gender-specific stereotypes in the data [ 40 ]. Similarly, a number of problematic cases of racial bias are also associated with AI’s facial recognition systems. Research shows that facial recognition software has improperly misidentified a number of African American and Latino American people as convicted felons [ 42 ].

Additionally, biased decision-making algorithms reveal themselves throughout AI applications in K-12 education: personalized learning, automated assessment, SNSs, and predictive systems in education. Although the main promise of machine-learning models is increased accuracy and objectivity, current incidents have revealed the contrary. For instance, England’s A-level and GCSE secondary level examinations were cancelled due to the pandemic in the summer of 2020 [ 1 , 57 ]. An alternative assessment method was implemented to determine the qualification grades of students. The grade standardization algorithm was produced by the regulator Ofqual. With the assessment of Ofqual’s algorithm based on schools' previous examination results, thousands of students were shocked to receive unexpectedly low grades. Although a full discussion of the incident is beyond the scope of this article [ 51 ] it revealed how the score distribution favored students who attended private or independent schools, while students from underrepresented groups were hit hardest. Unfortunately, automated assessment algorithms have the potential to reconstruct unfair and inconsistent results by disrupting student’s final scores and future careers [ 53 ].

Teaching and understanding AI and ethics in educational settings

These ethical concerns suggest an urgent need to introduce students and teachers to the ethical challenges surrounding AI applications in K-12 education and how to navigate them. To meet this need, different research groups and nonprofit organizations offer a number of open-access resources based on AI and ethics. They provide instructional materials for students and teachers, such as lesson plans and hands-on activities, and professional learning materials for educators, such as open virtual learning sessions. Below, we describe and evaluate three resources: “AI and Ethics” curriculum and “AI and Data Privacy” workshop from the Massachusetts Institute of Technology (MIT) Media Lab as well as Code.org’s “AI and Oceans” activity. For readers who seek to investigate additional approaches and resources for K-12 level AI and ethics interaction, see: (a) The Chinese University of Hong Kong (CUHK)’s AI for the Future Project (AI4Future) [ 18 ]; (b) IBM’s Educator’s AI Classroom Kit [ 30 ], Google’s Teachable Machine [ 25 ], UK-based nonprofit organization Apps for Good [ 4 ], and Machine Learning for Kids [ 37 ].

"AI and Ethics Curriulum" for middle school students by MIT Media Lab

The MIT Media Lab team offers an open-access curriculum on AI and ethics for middle school students and teachers. Through a series of lesson plans and hand-on activities, teachers are guided to support students’ learning of the technical terminology of AI systems as well as the ethical and societal implications of AI [ 2 ]. The curriculum includes various lessons tied to learning objectives. One of the main learning goals is to introduce students to basic components of AI through algorithms, datasets, and supervised machine-learning systems all while underlining the problem of algorithmic bias [ 45 ]. For instance, in the activity “ AI Bingo” , students are given bingo cards with various AI systems, such as online search engine, customer service bot, and weather app. Students work with their partners collaboratively on these AI systems. In their AI Bingo chart, students try to identify what prediction the selected AI system makes and what dataset it uses. In that way, they become more familiar with the notions of dataset and prediction in the context of AI systems [ 45 ].

In the second investigation, “Algorithms as Opinions” , students think about algorithms as recipes, which are created by set of instructions that modify an input to produce an output [ 45 ]. Initially, students are asked to write an algorithm to make the “ best” jelly sandwich and peanut butter. They explore what it means to be “ best” and see how their opinions of best in their recipes are reflected in their algorithms. In this way, students are able to figure out that algorithms can have various motives and goals. Following this activity, students work on the “Ethical Matrix” , building on the idea of the algorithms as opinions [ 45 ]. During this investigation, students first refer back to their developed algorithms through their “best” jelly sandwich and peanut butter. They discuss what counts as the “best” sandwich for themselves (most healthy, practical, delicious, etc.). Then, through their ethical matrix (chart), students identify different stakeholders (such as their parents, teacher, or doctor) who care about their peanut butter and jelly sandwich algorithm. In this way, the values and opinions of those stakeholders also are embedded in the algorithm. Students fill out an ethical matrix and look for where those values conflict or overlap with each other. This matrix is a great tool for students to recognize different stakeholders in a system or society and how they are able to build and utilize the values of the algorithms in an ethical matrix.

The final investigation which teaches about the biased nature of algorithms is “Learning and Algorithmic Bias” [ 45 ]. During the investigation, students think further about the concept of classification. Using Google’s Teachable Machine tool [ 2 ], students explore the supervised machine-learning systems. Students train a cat–dog classifier using two different datasets. While the first dataset reflects the cats as the over-represented group, the second dataset indicates the equal and diverse representation between dogs and cats [ 2 ]. Using these datasets, students compare the accuracy between the classifiers and then discuss which dataset and outcome are fairer. This activity leads students into a discussion about the occurrence of bias in facial recognition algorithms and systems [ 2 ].

In the rest of the curriculum, similar to the AI Bingo investigation, students work with their partners to determine the various forms of AI systems in the YouTube platform (such as its recommender algorithm and advertisement matching algorithm). Through the investigation of “ YouTube Redesign”, students redesign YouTube’s recommender system. They first identify stakeholders and their values in the system, and then use an ethical matrix to reflect on the goals of their YouTube’s recommendation algorithm [ 45 ]. Finally, through the activity of “YouTube Socratic Seminar” , students read an abridged version of Wall Street Journal article by participating in a Socratic seminar. The article was edited to shorten the text and to provide more accessible language for middle school students. They discuss which stakeholders were most influential or significant in proposing changes in the YouTube Kids app and whether or not technologies like auto play should ever exist. During their discussion, students engage with the questions of: “Which stakeholder is making the most change or has the most power?”, “Have you ever seen an inappropriate piece of content on YouTube? What did you do?” [ 45 ].

Overall, the MIT Media Lab’s AI and Ethics curriculum is a high quality, open-access resource with which teachers can introduce middle school students to the risks and ethical implications of AI systems. The investigations described above involve students in collaborative, critical thinking activities that force them to wrestle with issues of bias and discrimination in AI, as well as surveillance and autonomy through the predictive systems and algorithmic bias.

“AI and Data Privacy” workshop series for K-9 students by MIT Media Lab

Another quality resource from the MIT Media Lab’s Personal Robots Group is a workshop series designed to teach students (between the ages 7 and 14) about data privacy and introduce them to designing and prototyping data privacy features. The group has made the content, materials, worksheets, and activities of the workshop series into an open-access online document, freely available to teachers [ 44 ].

The first workshop in the series is “ Mystery YouTube Viewer: A lesson on Data Privacy” . During the workshop, students engage with the question of what privacy and data mean [ 44 ]. They observe YouTube’s home page from the perspective of a mystery user. Using the clues from the videos, students make predictions about what the characters in the videos might look like or where they might live. In a way, students imitate YouTube algorithms’ prediction mode about the characters. Engaging with these questions and observations, students think further about why privacy and boundaries are important and how each algorithm will interpret us differently based on who creates the algorithm itself.

The second workshop in the series is “ Designing ads with transparency: A creative workshop” . Through this workshop, students are able to think further about the meaning, aim, and impact of advertising and the role of advertisements in our lives [ 44 ]. Students collaboratively create an advertisement using an everyday object. The objective is to make the advertisement as “transparent” as possible. To do that, students learn about notions of malware and adware, as well as the components of YouTube advertisements (such as sponsored labels, logos, news sections, etc.). By the end of the workshop, students design their ads as a poster, and they share with their peers.

The final workshop in MIT’s AI and data privacy series is “ Designing Privacy in Social Media Platforms”. This workshop is designed to teach students about YouTube, design, civics, and data privacy [ 44 ]. During the workshop, students create their own designs to solve one of the biggest challenges of the digital era: problems associated with online consent. The workshop allows students to learn more about the privacy laws and how they impact youth in terms of media consumption. Students consider YouTube within the lenses of the Children’s Online Privacy Protections Rule (COPPA). In this way, students reflect on one of the components of the legislation: how might students get parental permission (or verifiable consent)?

Such workshop resources seem promising in helping educate students and teachers about the ethical challenges of AI in education. Specifically, social media such as YouTube are widely used as a teaching and learning tool within K-12 classrooms and beyond them, in students’ everyday lives. These workshop resources may facilitate teachers’ and students’ knowledge of data privacy issues and support them in thinking further about how to protect privacy online. Moreover, educators seeking to implement such resources should consider engaging students in the larger question: who should own one’s data? Teaching students the underlying reasons for laws and facilitating debate on the extent to which they are just or not could help get at this question.

Investigation of “AI for Oceans” by Code.org

A third recommended resource for K-12 educators trying to navigate the ethical challenges of AI with their students comes from Code.org, a nonprofit organization focused on expanding students’ participation in computer science. Sponsored by Microsoft, Facebook, Amazon, Google, and other tech companies, Code.org aims to provide opportunities for K-12 students to learn about AI and machine-learning systems [ 20 ]. To support students (grades 3–12) in learning about AI, algorithms, machine learning, and bias, the organization offers an activity called “ AI for Oceans ”, where students are able to train their machine-learning models.

The activity is provided as an open-access tutorial for teachers to help their students explore how to train, model and classify data , as well as to understand how human bias plays a role in machine-learning systems. During the activity, students first classify the objects as either “fish” or “not fish” in an attempt to remove trash from the ocean. Then, they expand their training data set by including other sea creatures that belong underwater. Throughout the activity, students are also able to watch and interact with a number of visuals and video tutorials. With the support of their teachers, they discuss machine learning, steps and influences of training data, as well as the formation and risks of biased data [ 20 ].

Future directions for research and teaching on AI and ethics

In this paper, we provided an overview of the possibilities and potential ethical and societal risks of AI integration in education. To help address these risks, we highlighted several instructional strategies and resources for practitioners seeking to integrate AI applications in K-12 education and/or instruct students about the ethical issues they pose. These instructional materials have the potential to help students and teachers reap the powerful benefits of AI while navigating ethical challenges especially related to privacy concerns and bias. Existing research on AI in education provides insight on supporting students’ understanding and use of AI [ 2 , 13 ]; however, research on how to develop K-12 teachers’ instructional practices regarding AI and ethics is still in its infancy.

Moreover, current resources, as demonstrated above, mainly address privacy and bias-related ethical and societal concerns of AI. Conducting more exploratory and critical research on teachers’ and students’ surveillance and autonomy concerns will be important to designing future resources. In addition, curriculum developers and workshop designers might consider centering culturally relevant and responsive pedagogies (by focusing on students’ funds of knowledge, family background, and cultural experiences) while creating instructional materials that address surveillance, privacy, autonomy, and bias. In such student-centered learning environments, students voice their own cultural and contextual experiences while trying to critique and disrupt existing power structures and cultivate their social awareness [ 24 , 36 ].

Finally, as scholars in teacher education and educational technology, we believe that educating future generations of diverse citizens to participate in the ethical use and development of AI will require more professional development for K-12 teachers (both pre-service and in-service). For instance, through sustained professional learning sessions, teachers could engage with suggested curriculum resources and teaching strategies as well as build a community of practice where they can share and critically reflect on their experiences with other teachers. Further research on such reflective teaching practices and students’ sense-making processes in relation to AI and ethics lessons will be essential to developing curriculum materials and pedagogies relevant to a broad base of educators and students.

This work was supported by the Graduate School at Michigan State University, College of Education Summer Research Fellowship.

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Selin Akgun, Email: ude.usm@lesnugka .

Christine Greenhow, Email: ude.usm@wohneerg .

Artificial Intelligence and Its Impact on Education Essay

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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Essay on the Negative Effects of Artificial Intelligence

In a technological conference in Austin, Texas, tech mogul Elon Must issued a stern warning to the audience. He begins his message by stating that people should mark his words and that artificial intelligence is more dangerous than nuclear weapons. Before people get ahead of this premonition and start preparations for the hostile takeover of machines, I wish to reiterate that machines have not yet taken over, not yet at least. Despite this dark premise, it is undeniable that technology has increasingly integrated itself into many aspects of daily living. The experience of human living can no longer be described without the mention of technological contributions. Technology affects how people work, live, rest, and entertain themselves. Technology can be found anywhere, whether at home, school, mall, and office. From virtual assistants like Google and Siri to global positioning system apps like Waze that help people navigate traffic, technology has steadily seeped into people’s lives. Artificial intelligence presents good results in society and will continue to benefit the technologically reliant world people enjoy. Despite the many advantages of AI, there are still several disadvantages that AI may bring.  Black Mirror  highlights the dystopian effects of AI and how the development of advanced AI machines will degrade society. An example is the episode “Nosedive.” In this episode, a social media application determines a “social score” depending on the good deeds the user has posted in the app. Since each citizen has the application installed on their phones, the social score in the app determines the perks one will receive, such as discounts in stores, chances of securing a home loan, and many more. In this episode, the application dictated the protagonist’s faith, revealing how excessive reliance on social media may deteriorate people’s social norms. In a different episode called “Be Right Back,” the story tackles how AI has progressed so that it can bring the dead back to life. The AI accomplishes this by recording and analyzing all messages, calls, and personal records of the deceased person. In this episode, the widow avails this service to bring her husband “back to life.” She later realizes that the android version can never replace him, thus leaving her miserable. These are only two examples in which the development of AI led to social rating systems and android humans negatively affected people’s lives.  Black Mirror  is a show that tackles the social commentary on how technology, when left unchecked, may manifest in forms that will disrupt and alter human living altogether. As I said at the beginning of this essay, technology has not yet taken over. However, it is undeniable that technology is constantly developing and improving. It is essential that people research AI technology well to prevent the futuristic problems  Black Mirror  presents. When placed in a hypothetical timeline, Black Mirror is the future that awaits technological development devoid of limitations and checks. The contemporary era is the present wherein people have the chance to identify the problems in AI and immediately remedy them in such a way as to prevent a “Black Mirror-like” future. AI is a complex construct that people cannot fully grasp yet. The lack of knowledge and deeper understanding of AI allows for errors or adverse consequences to occur. The essay will discuss the current problems in artificial intelligence, including challenges in AI development, emerging security threats, and the development of superintelligence.

A TED Talk by Janelle Shane tackles one key issue in AI development. In this video, Shane shares her experiences in AI development. The central issue in AI development is that AI machines do not execute the developers’ actions. This premise means that when AI developers feed the machine data and instructions, the machine interprets this set of instructions differently from how developers planned to understand the instruction. For example, an AI machine was programmed to move fast from point A to point B. While the instructions are simple enough, the machine interpreted this differently. The machine moved to do summersaults, twitched, and rolled to move fast from point A to B. The developers fed the machine data in hopes that it would jog or run from point A to B, but instead, the machine executed different sets of emotions that still met the criteria of “moving fast.” Another example given in Shane’s TED talks was when Amazon introduced a resume sorting algorithm and said algorithm learned to discriminate against resumes with the word women written on them. The algorithm created by Amazon was fed data from previous resumes of people who worked in Amazon. Since most of the resumes were from male candidates, the algorithm accidentally learned that women candidates are inferior to men, thus identifying specific sex as inferior. While the AI does not comprehend social constructs and cultural ideologies, it will always accomplish the task it was made to do based on the data and programming it receives (Shane). The central problem is how AI can interpret instructions very differently from what programmers and developers imagine. We must remember that AI will always do what it is asked to accomplish; the problem lies wherein people accidentally ask the AI to do the wrong thing (Shane). The problem arises because AI often commits these errors, which leads to accidental occurrences that may promote adverse outcomes. This TED talk by Shane reveals that AI developers must be careful and strict in their data programming so that errors may occur less. Knowledge in AI is built to improve lives, but glitches and data misinterpretations may lead to dire consequences that create adverse outcomes.

Another issue in AI technology is the emergence of security threats in a person’s data. Data is the heart and soul of all artificial intelligence machines. An article by Joseph Mutschelknaus tackles the top 5 data privacy issues in the emergence of AI technology. Data is essential to any AI operations because data is needed to train machines and algorithms into performing tasks (Mutschelknaus). This premise shows that data from people or events are harvested and utilized with each operating AI machine or algorithm. The problem arises when data is used for malicious intent. One prominent example of the misuse of data is the emergence of deepfake pictures and videos. John Villasenor tackles the issues of deepfake media content and how this technology may be abused. Deepfakes are media content constructed to make the person appear to be saying or doing something that he or she has never done before (Villasenor). The term “deepfake” emerges from the combination of two words, deep learning and fake. Deep learning refers to an AI learning process in which data is fed to the machine to analyze and memorize. What deepfake aims to present videos or pictures that look and sound natural, but AI devices have edited these media contents. There are many types of deepfake technologies currently in existence. Some examples include face-swapping. In face swapping, a person’s face is swapped for the body of a different person, and lip-syncing, in which an AI algorithm alters a person’s lip movements and imposes a different audio track to their speech. Bernice Donald and Ronald Hedges tackle creating deepfake technology and how deepfakes have evolved with the technological advancement of AI. Deepfakes are created by AI algorithms or machine learning programs in which large data sets of images or sound clips are analyzed, reconstructed, and edited by the program (Donald and Hedges). While initial deepfake photos and videos were challenging to create in the past, technological advances in machine learning and new software have made the creation of deepfakes convenient (Donald and Hedges). What is concerning in deepfakes is that they are challenging to identify. Deepfakes in social media are screened and removed, but research has shown that only two-thirds of all deepfakes are identified as fake pictures (Donald and Hedges). What follows is the malicious attempts to utilize the deepfake technology. Circling back to Villasenor’s article on the negative consequences of deepfake technology, deepfakes can be used against politicians or famous personalities by manipulating videos. These personalities are made to say things that could harm their reputations (Villasenor). Furthermore, deepfakes are becoming prominent in pornographic websites in which people superimpose the faces of artists and other people into obscene videos (Villasenor). The implications of dealing with deepfake technology become complex because people cannot distinguish real from fake. Deepfakes scramble our notion of the truth by exploiting our drive to believe what we see first-hand. In trying to fend off deepfake propaganda, an individual’s trust in all video and photographic content becomes tainted because it is increasingly difficult to discern real from fake. This premise reveals that AI advancement drives humanity in a situation where we cannot reliably believe our own eyes and ears in processing content. This situation becomes increasingly dangerous when malicious people employ deepfakes for events that lead to grave consequences such as war and international conflict. What makes the situation worse is that legislation against deepfake technologies has not been drafted; thus, deepfake remains increasingly difficult to handle.

Another pressing issue that humans face during AI development is the eventual birth of super-intelligent AI. Sam Harris outlines super-intelligent AI in his TED talk as an irrefutable future that humans will undergo. Harris opens his talk by providing two options; one option offers people to stop creating AI technology advancements. This option appears to be unfavorable given how much technology has improved human living. Apart from this, people still desire to create machines that will solve cancer and climate research problems that we are yet to discover. Harris also outlines that it would take a global crisis such as a pandemic or nuclear warfare to halt the development of new technology altogether. Harrisreiterates by saying, “You have to imagine how bad it would have to be to prevent us from making improvements in our technology permanently.” This premise leaves people to choose the second door. In the second door, people continue to develop AI technology, and in this effort, it is predicted that AI will develop a super-intelligent machine that transcends human intelligence (Harris). In the development of super-intelligent machines, the primary concern is whether the machine that surpasses its creators may coexist. He explains that if the goals of super-intelligent machines diverge from the goals of humans, super-intelligent machines may not hesitate to eradicate humans. An analogy he gives pertains to ants. People do not actively want to harm ants and often try to avoid harming them. However, if the ants were in a land where construction of a new building would occur, humans would immediately eradicate the ants in said land. The problem is not that super-intelligent machines will spontaneously become malevolent; it is that superintelligence is far superior to humans that we may not predict when our goals will diverge from the super-intelligent machines. The development of super-intelligent machines is inevitable, and while experts speculate that this form of technology is many years away, it is still a possibility awaiting the future generation.

To conclude, humans’ current AI technology is not yet up to par with “Black Mirror” technology. As experts continuously reassure people that machines will not take over the world, people need to think about the possible adverse effects of technological advancement. Issues in AI development, deepfakes, and superintelligence are only some issues that affect AI advancement. There are many other issues concerning AI development. In trying to achieve significant accomplishments, there will always be hurdles that will make the journey rough. Technology is already a part of human living that cannot be undone. People can now refine the research on AI machines to prevent the adverse effects that may lead to unprecedented outcomes in the future. Experts in AI must ensure that ethical standards and safety checks are thoroughly considered in developing AI technology. As it stands, people are tinkering with an object that has the potential to surpass human intelligence; with this knowledge, it is ultimately up to humans to determine what the future holds.

Works Cited

Adams, R. “10 Powerful Examples Of Artificial Intelligence In Use Today.”  Forbes , 10 Jan. 2017, www.forbes.com/sites/robertadams/2017/01/10/10-powerful-examples-of-artificial-intelligence-in-use-today/?sh=77f35725420d. Accessed 18 Apr. 2021.

Donald, Bernice B., and Ronald J. Hedges. “Deepfakes Bring New Privacy and Cybersecurity Concerns.”  Corporate Counsel Business Journal , 25 Sept. 2020, ccbjournal.com/articles/deepfakes-bring-new-privacy-and-cybersecurity-concerns. Accessed 30 Apr. 2021.

Faggella, Daniel. “Everyday Examples of Artificial Intelligence and Machine Learning.”  Emerj , 11 Apr. 2020, emerj.com/ai-sector-overviews/everyday-examples-of-ai/. Accessed 18 Apr. 2021.

Harris, Sam. “Can we build AI without losing control over it?”  Youtube , TED, 20 Oct. 2016, www.youtube.com/watch?v=8nt3edWLgIg. Accessed 18 Apr. 2021.

Mutschelknaus, Joseph E. “Top Five Data Privacy Issues that Artificial Intelligence and Machine Learning Startups Need to Know.”  Inside Big Data , 23 July 2020, insidebigdata.com/2020/07/23/top-five-data-privacy-issues-that-artificial-intelligence-and-machine-learning-startups-need-to-know/. Accessed 30 Apr. 2021.

Shane, Janelle. “The danger of AI is weirder than you think.”  Youtube , TED, 14 Nov. 2019, www.youtube.com/watch?v=OhCzX0iLnOc. Accessed 18 Apr. 2021.

Villasenor, John D. “Artificial intelligence, deepfakes, and the uncertain future of truth.”  Brookings , 14 Feb. 2019, www.brookings.edu/blog/techtank/2019/02/14/artificial-intelligence-deepfakes-and-the-uncertain-future-of-truth/. Accessed 30 Apr. 2021.

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The Impact of Artificial Intelligence and ChatGPT on Education

The deployment of ChatGPT, an artificial intelligence (AI) application that can compose grade–passing essays, create detailed art and even engage in philosophical conversations and write computer code for specific tasks, is raising tough questions about the future of AI in education and a variety of industries.

Will AI replace or stunt human intellect or critical thinking? Will cheating cripple the learning model in higher education?

When it comes to education, the downside is that ultimately the students are not learning or developing their writing skills with the assistance of such technologies. In addition, not all schools and teaching programs are designed with cutting-edge technologies, making it difficult to even detect when a student uses a tool like ChatGPT.

On the flip side, AI applications like ChatGPT have the potential to make people’s lives easier and to assist with mundane and time-consuming tasks that do not assist with thinking or intellectual growth, such as writing emails or searching endlessly on the internet for information.

There are also several areas AI can assist educators and further improve education:

• Writer’s block – AI could assist with writer’s block to generate more original or nuanced arguments.
• Summarizing vast research – Imagine being able to summarize a ton of research into succinct abstracts, saving hours of time and effort, allowing one to focus on the most relevant and important information quickly.
• Exam preparation – Generating practice exams or questions to test and prepare or personalized feedback on written assignments from ChatGPT to further improve writing skills.
• Guided feedback – AI may also have the potential to provide guided feedback in real time, which would be particularly beneficial for larger class sizes.
• Lesson preparation – Generating lesson plans and teaching materials, allowing educators to save time and effort preparing for class.

Hope for the future of education

The current educational model has always been augmented by new technology, and this was accelerated during the COVID-19 pandemic due to distance learning and online models. All education must continue to be reviewed and keep pace with the evolution of different tools and technologies. Usage of new technology is always a balancing act; perhaps we need to think of ways we can utilize artificial intelligence in the most productive fashion to build something better.

What is the right question to ask? What is a big problem we can address using artificial intelligence tools like ChatGPT for the betterment of society? Some examples may include addressing climate change more effectively through more efficient uses of natural resources, space exploration or curing cancer. If we can now synthesize and interpret vast information, we need to think of pointing that to the right problems and push humanity forward in the best way possible.

The University of St. Thomas is striving to be at the forefront of this change. In so doing, we have revamped our Digital Transformation course to include generative AI technologies into the course with analysis of a digital maturity model for organizations to assess gaps with this technology, prompt engineering, and creation of a framework for organizational governance. With the right compass, we can harness the technology for good by saving vast amounts of time on time-consuming functions to enable breakthroughs. The correct lens is to look at supercharging or speeding up laborious activities of the past to vastly improve productivity.

Author bios:

Manjeet Rege is a professor and chair of the Department of Software and Data Science at the University of St. Thomas. Rege is an author, mentor, thought leader, and a frequent public speaker on big data, machine learning, and artificial intelligence technologies. He is also the co-host of the “All Things Data” podcast that brings together leading data scientists, technologists, business model experts and futurists to discuss strategies to utilize, harness and deploy data science, data-driven strategies and enable digital transformation. Apart from being engaged in research, Rege regularly consults with various organizations to provide expert guidance for building big data and AI practice, and applying innovative data science approaches. He has published in various peer-reviewed reputed venues such as IEEE Transactions on Knowledge and Data Engineering, Data Mining & Knowledge Discovery Journal, IEEE International Conference on Data Mining, and the World Wide Web Conference. He is on the editorial review board of Journal of Computer Information Systems and regularly serves on the program committees of various international conferences.

Dan Yarmoluk has been involved in analytics, embedded design and components of mobile products for over a decade. He has focused on creating and driving automation with technology, analytics, and business models that intersect to drive added value and digital transformation. Industries he has served include manufacturing, health care, financial, real estate, construction and education. He publishes his thoughts frequently and co-hosts a popular podcast “All Things Data” with Dr. Manjeet Rege of the University of St. Thomas.

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