artificial intelligence negative impacts 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.

SQ10. What are the most pressing dangers of AI?

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As AI systems prove to be increasingly beneficial in real-world applications, they have broadened their reach, causing risks of misuse, overuse, and explicit abuse to proliferate. As AI systems increase in capability and as they are integrated more fully into societal infrastructure, the implications of losing meaningful control over them become more concerning. 1 New research efforts are aimed at re-conceptualizing the foundations of the field to make AI systems less reliant on explicit, and easily misspecified, objectives. 2 A particularly visible danger is that AI can make it easier to build machines that can spy and even kill at scale . But there are many other important and subtler dangers at present.

In this section

Techno-solutionism, dangers of adopting a statistical perspective on justice, disinformation and threat to democracy, discrimination and risk in the medical setting.

One of the most pressing dangers of AI is techno-solutionism, the view that AI can be seen as a panacea when it is merely a tool. 3 As we see more AI advances, the temptation to apply AI decision-making to all societal problems increases. But technology often creates larger problems in the process of solving smaller ones. For example, systems that streamline and automate the application of social services can quickly become rigid and deny access to migrants or others who fall between the cracks. 4

When given the choice between algorithms and humans, some believe algorithms will always be the less-biased choice. Yet, in 2018, Amazon found it necessary to discard a proprietary recruiting tool because the historical data it was trained on resulted in a system that was systematically biased against women. 5 Automated decision-making can often serve to replicate, exacerbate, and even magnify the same bias we wish it would remedy.

Indeed, far from being a cure-all, technology can actually create feedback loops that worsen discrimination. Recommendation algorithms, like Google’s page rank, are trained to identify and prioritize the most “relevant” items based on how other users engage with them. As biased users feed the algorithm biased information, it responds with more bias, which informs users’ understandings and deepens their bias, and so on. 6 Because all technology is the product of a biased system, 7 techno-solutionism’s flaws run deep: 8 a creation is limited by the limitations of its creator.

Automated decision-making may produce skewed results that replicate and amplify existing biases. A potential danger, then, is when the public accepts AI-derived conclusions as certainties. This determinist approach to AI decision-making can have dire implications in both criminal and healthcare settings. AI-driven approaches like PredPol, software originally developed by the Los Angeles Police Department and UCLA that purports to help protect one in 33 US citizens, 9 predict when, where, and how crime will occur. A 2016 case study of a US city noted that the approach disproportionately projected crimes in areas with higher populations of non-white and low-income residents. 10 When datasets disproportionately represents the lower power members of society, flagrant discrimination is a likely result.

Sentencing decisions are increasingly decided by proprietary algorithms that attempt to assess whether a defendant will commit future crimes, leading to concerns that justice is being outsourced to software. 11 As AI becomes increasingly capable of analyzing more and more factors that may correlate with a defendant's perceived risk, courts and society at large may mistake an algorithmic probability for fact. This dangerous reality means that an algorithmic estimate of an individual’s risk to society may be interpreted by others as a near certainty—a misleading outcome even the original tool designers warned against. Even though a statistically driven AI system could be built to report a degree of credence along with every prediction, 12 there’s no guarantee that the people using these predictions will make intelligent use of them. Taking probability for certainty means that the past will always dictate the future.

There is an aura of neutrality and impartiality associated with AI decision-making in some corners of the public consciousness, resulting in systems being accepted as objective even though they may be the result of biased historical decisions or even blatant discrimination. All data insights rely on some measure of interpretation. As a concrete example, an audit of a resume-screening tool found that the two main factors it associated most strongly with positive future job performance were whether the applicant was named Jared, and whether he played high school lacrosse. 13 Undesirable biases can be hidden behind both the opaque nature of the technology used and the use of proxies, nominally innocent attributes that enable a decision that is fundamentally biased. An algorithm fueled by data in which gender, racial, class, and ableist biases are pervasive can effectively reinforce these biases without ever explicitly identifying them in the code. 

Without transparency concerning either the data or the AI algorithms that interpret it, the public may be left in the dark as to how decisions that materially impact their lives are being made. Lacking adequate information to bring a legal claim, people can lose access to both due process and redress when they feel they have been improperly or erroneously judged by AI systems. Large gaps in case law make applying Title VII—the primary existing legal framework in the US for employment discrimination—to cases of algorithmic discrimination incredibly difficult. These concerns are exacerbated by algorithms that go beyond traditional considerations such as a person’s credit score to instead consider any and all variables correlated to the likelihood that they are a safe investment. A statistically significant correlation has been shown among Europeans between loan risk and whether a person uses a Mac or PC and whether they include their name in their email address—which turn out to be proxies for affluence. 14 Companies that use such attributes, even if they do indeed provide improvements in model accuracy, may be breaking the law when these attributes also clearly correlate with a protected class like race. Loss of autonomy can also result from AI-created “information bubbles” that narrowly constrict each individual’s online experience to the point that they are unaware that valid alternative perspectives even exist.

AI systems are being used in the service of disinformation on the internet, giving them the potential to become a threat to democracy and a tool for fascism. From deepfake videos to online bots manipulating public discourse by feigning consensus and spreading fake news, 15 there is the danger of AI systems undermining social trust. The technology can be co-opted by criminals, rogue states, ideological extremists, or simply special interest groups, to manipulate people for economic gain or political advantage. Disinformation poses serious threats to society, as it effectively changes and manipulates evidence to create social feedback loops that undermine any sense of objective truth. The debates about what is real quickly evolve into debates about who gets to decide what is real, resulting in renegotiations of power structures that often serve entrenched interests. 16

While personalized medicine is a good potential application of AI, there are dangers. Current business models for AI-based health applications tend to focus on building a single system—for example, a deterioration predictor—that can be sold to many buyers. However, these systems often do not generalize beyond their training data. Even differences in how clinical tests are ordered can throw off predictors, and, over time, a system’s accuracy will often degrade as practices change. Clinicians and administrators are not well-equipped to monitor and manage these issues, and insufficient thought given to the human factors of AI integration has led to oscillation between mistrust of the system (ignoring it) and over-reliance on the system (trusting it even when it is wrong), a central concern of the 2016 AI100 report.

These concerns are troubling in general in the high-risk setting that is healthcare, and even more so because marginalized populations—those that already face discrimination from the health system from both structural factors (like lack of access) and scientific factors (like guidelines that were developed from trials on other populations)—may lose even more. Today and in the near future, AI systems built on machine learning are used to determine post-operative personalized pain management plans for some patients and in others to predict the likelihood that an individual will develop breast cancer. AI algorithms are playing a role in decisions concerning distributing organs, vaccines, and other elements of healthcare. Biases in these approaches can have literal life-and-death stakes.

In 2019, the story broke that Optum, a health-services algorithm used to determine which patients may benefit from extra medical care, exhibited fundamental racial biases. The system designers ensured that race was precluded from consideration, but they also asked the algorithm to consider the future cost of a patient to the healthcare system. 17 While intended to capture a sense of medical severity, this feature in fact served as a proxy for race: controlling for medical needs, care for Black patients averages $1,800 less per year.

New technologies are being developed every day to treat serious medical issues. A new algorithm trained to identify melanomas was shown to be more accurate than doctors in a recent study, but the potential for the algorithm to be biased against Black patients is significant as the algorithm was trained using majority light-skinned groups. 18 The stakes are especially high for melanoma diagnoses, where the five-year survival rate is 17 percentage points less for Black Americans than white. While technology has the potential to generate quicker diagnoses and thus close this survival gap, a machine-learning algorithm is only as good as its data set. An improperly trained algorithm could do more harm than good for patients at risk, missing cancers altogether or generating false positives. As new algorithms saturate the market with promises of medical miracles, losing sight of the biases ingrained in their outcomes could contribute to a loss of human biodiversity, as individuals who are left out of initial data sets are denied adequate care. While the exact long-term effects of algorithms in healthcare are unknown, their potential for bias replication means any advancement they produce for the population in aggregate—from diagnosis to resource distribution—may come at the expense of the most vulnerable.

[1]  Brian Christian, The Alignment Problem: Machine Learning and Human Values, W. W. Norton & Company, 2020

[2]   https://humancompatible.ai/app/uploads/2020/11/CHAI-2020-Progress-Report-public-9-30.pdf  

[3]   https://knightfoundation.org/philanthropys-techno-solutionism-problem/  

[4]   https://www.theguardian.com/world/2021/jan/12/french-woman-spends-three-years-trying-to-prove-she-is-not-dead ; https://virginia-eubanks.com/ (“Automating inequality”)

[5]   https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G

[6]  Safiya Umoja Noble, Algorithms of Oppression: How Search Engines Reinforce Racism , NYU Press, 2018 

[7]  Ruha Benjamin, Race After Technology: Abolitionist Tools for the New Jim Code , Polity, 2019

[8]   https://www.publicbooks.org/the-folly-of-technological-solutionism-an-interview-with-evgeny-morozov/

[9]   https://predpol.com/about  

[10]  Kristian Lum and William Isaac, “To predict and serve?” Significance , October 2016, https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/j.1740-9713.2016.00960.x

[11]  Jessica M. Eaglin, “Technologically Distorted Conceptions of Punishment,” https://www.repository.law.indiana.edu/cgi/viewcontent.cgi?article=3862&context=facpub  

[12]  Riccardo Fogliato, Maria De-Arteaga, and Alexandra Chouldechova, “Lessons from the Deployment of an Algorithmic Tool in Child Welfare,” https://fair-ai.owlstown.net/publications/1422  

[13]   https://qz.com/1427621/companies-are-on-the-hook-if-their-hiring-algorithms-are-biased/  

[14]   https://www.fdic.gov/analysis/cfr/2018/wp2018/cfr-wp2018-04.pdf  

[15]  Ben Buchanan, Andrew Lohn, Micah Musser, and Katerina Sedova, “Truth, Lies, and Automation,” https://cset.georgetown.edu/publication/truth-lies-and-automation/  

[16]  Britt Paris and Joan Donovan, “Deepfakes and Cheap Fakes: The Manipulation of Audio and Visual Evidence,” https://datasociety.net/library/deepfakes-and-cheap-fakes/  

[17]   https://www.washingtonpost.com/health/2019/10/24/racial-bias-medical-algorithm-favors-white-patients-over-sicker-black-patients/ .

[18]   https://www.theatlantic.com/health/archive/2018/08/machine-learning-dermatology-skin-color/567619/

Cite This Report

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

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

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Ethical concerns mount as AI takes bigger decision-making role in more industries

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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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

.chakra .wef-1c7l3mo{-webkit-transition:all 0.15s ease-out;transition:all 0.15s ease-out;cursor:pointer;-webkit-text-decoration:none;text-decoration:none;outline:none;color:inherit;}.chakra .wef-1c7l3mo:hover,.chakra .wef-1c7l3mo[data-hover]{-webkit-text-decoration:underline;text-decoration:underline;}.chakra .wef-1c7l3mo:focus,.chakra .wef-1c7l3mo[data-focus]{box-shadow:0 0 0 3px rgba(168,203,251,0.5);} Toby Walsh

Liz sonenberg.

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Stay up to date:, emerging technologies.

• 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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License and Republishing

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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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] .

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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.”

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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.”

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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?”

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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.”

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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 .

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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.”

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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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• v.32(4); Oct-Dec 2020

The impact of artificial intelligence on human society and bioethics

Michael cheng-tek tai.

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

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

W HAT IS ARTIFICIAL INTELLIGENCE ?

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

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

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

D IFFERENT TYPES OF ARTIFICIAL INTELLIGENCE

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

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

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

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

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

D O HUMAN-BEINGS REALLY NEED ARTIFICIAL INTELLIGENCE ?

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

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

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

T HE IMPACT OF ARTIFICIAL INTELLIGENCE ON HUMAN SOCIETY

Negative impact.

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

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

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

P OSITIVE IMPACT

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

Fast and accurate diagnostics

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

Socially therapeutic robots

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

Reduce errors related to human fatigue

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

Artificial intelligence-based surgical contribution

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

Improved radiology

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

Virtual presence

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

S OME CAUTIONS TO BE REMINDED

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

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

T HE CHALLENGE OF ARTIFICIAL INTELLIGENCE TO BIOETHICS

Artificial intelligence ethics must be developed.

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

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

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

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

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

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

Seven requirements are recommended [ 18 ]:

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

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

S UGGESTED PRINCIPLES FOR ARTIFICIAL INTELLIGENCE BIOETHICS

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

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

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

C ONCLUSION

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

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Positive and Negative Impacts of Artificial Intelligence

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Table of Contents

What is artificial intelligence, advantages and disadvantages of artificial intelligence, 10 benefits of artificial intelligence, disadvantages of artificial intelligence, advantages and disadvantages of ai in different sectors and industries, choose the right program, advantages and disadvantages of artificial intelligence - the bottom line, advantages and disadvantages of artificial intelligence [ai].

Reviewed and fact-checked by Sayantoni Das

With all the hype around Artificial Intelligence - robots, self-driving cars , etc. - it can be easy to assume that AI doesn’t impact our everyday lives. In reality, most of us encounter Artificial Intelligence in some way or the other almost every single day. From the moment you wake up to check your smartphone to watching another Netflix recommended movie, AI has quickly made its way into our everyday lives. According to a study by Statista, the global AI market is set to grow up to 54 percent every single year . But what exactly is AI? Will it really serve good to mankind in the future? Well, there are tons of advantages and disadvantages of Artificial Intelligence which we’ll discuss in this article. But before we jump into the pros and cons of AI, let us take a quick glance over what is AI.

Before we jump on to the advantages and disadvantages of Artificial Intelligence, let us understand what is AI in the first place. From a birds eye view, AI provides a computer program the ability to think and learn on its own. It is a simulation of human intelligence (hence, artificial) into machines to do things that we would normally rely on humans. This technological marvel extends beyond mere automation, incorporating a broad spectrum of AI skills - abilities that enable machines to understand, reason, learn, and interact in a human-like manner. There are three main types of AI based on its capabilities - weak AI, strong AI, and super AI.

• Weak AI - Focuses on one task and cannot perform beyond its limitations (common in our daily lives)
• Strong AI - Can understand and learn any intellectual task that a human being can (researchers are striving to reach strong AI)
• Super AI - Surpasses human intelligence and can perform any task better than a human (still a concept)

Here's a quick video to help you understand what artificial intelligence is and understand its advantages and disadvantages. 

An artificial intelligence program is a program that is capable of learning and thinking. It is possible to consider anything to be artificial intelligence if it consists of a program performing a task that we would normally assume a human would perform.

While artificial intelligence has many benefits, there are also drawbacks. The benefits of AI include efficiency through task automation, data analysis for informed decisions, assistance in medical diagnosis, and the advancement of autonomous vehicles. The drawbacks of AI include job displacement, ethical concerns about bias and privacy, security risks from hacking, a lack of human-like creativity and empathy.

Let's begin with the advantages of artificial intelligence.

1. Reduction in Human Error

One of the biggest benefits of Artificial Intelligence is that it can significantly reduce errors and increase accuracy and precision. The decisions taken by AI in every step is decided by information previously gathered and a certain set of algorithms . When programmed properly, these errors can be reduced to null. 

An example of the reduction in human error through AI is the use of robotic surgery systems, which can perform complex procedures with precision and accuracy, reducing the risk of human error and improving patient safety in healthcare.

2. Zero Risks

Another big benefit of AI is that humans can overcome many risks by letting AI robots do them for us. Whether it be defusing a bomb, going to space, exploring the deepest parts of oceans, machines with metal bodies are resistant in nature and can survive unfriendly atmospheres. Moreover, they can provide accurate work with greater responsibility and not wear out easily.

One example of zero risks is a fully automated production line in a manufacturing facility. Robots perform all tasks, eliminating the risk of human error and injury in hazardous environments.

3. 24x7 Availability

There are many studies that show humans are productive only about 3 to 4 hours in a day. Humans also need breaks and time offs to balance their work life and personal life. But AI can work endlessly without breaks. They think much faster than humans and perform multiple tasks at a time with accurate results. They can even handle tedious repetitive jobs easily with the help of AI algorithms. 

An example of this is online customer support chatbots, which can provide instant assistance to customers anytime, anywhere. Using AI and natural language processing, chatbots can answer common questions, resolve issues, and escalate complex problems to human agents, ensuring seamless customer service around the clock.

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4. Digital Assistance

Some of the most technologically advanced companies engage with users using digital assistants, which eliminates the need for human personnel. Many websites utilize digital assistants to deliver user-requested content. We can discuss our search with them in conversation. Some chatbots are built in a way that makes it difficult to tell whether we are conversing with a human or a chatbot.

We all know that businesses have a customer service crew that must address the doubts and concerns of the patrons. Businesses can create a chatbot or voice bot that can answer all of their clients' questions using AI.

Related Reading: Top Digital Marketing Trends

5. New Inventions

In practically every field, AI is the driving force behind numerous innovations that will aid humans in resolving the majority of challenging issues.

For instance, recent advances in AI-based technologies  have allowed doctors to detect breast cancer in a woman at an earlier stage.

Another example of new inventions is self-driving cars, which use a combination of cameras, sensors, and AI algorithms to navigate roads and traffic without human intervention. Self-driving cars have the potential to improve road safety, reduce traffic congestion, and increase accessibility for people with disabilities or limited mobility. They are being developed by various companies, including Tesla, Google, and Uber, and are expected to revolutionize transportation.

6. Unbiased Decisions

Human beings are driven by emotions, whether we like it or not. AI on the other hand, is devoid of emotions and highly practical and rational in its approach. A huge advantage of Artificial Intelligence is that it doesn't have any biased views, which ensures more accurate decision-making.

An example of this is AI-powered recruitment systems that screen job applicants based on skills and qualifications rather than demographics. This helps eliminate bias in the hiring process, leading to an inclusive and more diverse workforce.

7. Perform Repetitive Jobs

We will be doing a lot of repetitive tasks as part of our daily work, such as checking documents for flaws and mailing thank-you notes, among other things. We may use artificial intelligence to efficiently automate these menial chores and even eliminate "boring" tasks for people, allowing them to focus on being more creative.

An example of this is using robots in manufacturing assembly lines, which can handle repetitive tasks such as welding, painting, and packaging with high accuracy and speed, reducing costs and improving efficiency.

8. Daily Applications

Today, our everyday lives are entirely dependent on mobile devices and the internet. We utilize a variety of apps, including Google Maps, Alexa, Siri, Cortana on Windows, OK Google, taking selfies, making calls, responding to emails, etc. With the use of various AI-based techniques, we can also anticipate today’s weather and the days ahead.

About 20 years ago, you must have asked someone who had already been there for instructions when you were planning a trip. All you need to do now is ask Google where Bangalore is. The best route between you and Bangalore will be displayed, along with Bangalore's location, on a Google map.

9. AI in Risky Situations

One of the main benefits of artificial intelligence is this. By creating an AI robot that can perform perilous tasks on our behalf, we can get beyond many of the dangerous restrictions that humans face. It can be utilized effectively in any type of natural or man-made calamity, whether it be going to Mars, defusing a bomb, exploring the deepest regions of the oceans, or mining for coal and oil.

For instance, the explosion at the Chernobyl nuclear power facility in Ukraine. As any person who came close to the core would have perished in a matter of minutes, at the time, there were no AI-powered robots that could assist us in reducing the effects of radiation by controlling the fire in its early phases.

10. Medical Applications

AI has also made significant contributions to the field of medicine, with applications ranging from diagnosis and treatment to drug discovery and clinical trials. AI-powered tools can help doctors and researchers analyze patient data, identify potential health risks, and develop personalized treatment plans. This can lead to better health outcomes for patients and help accelerate the development of new medical treatments and technologies.

Let us now look at what are the main disadvantages that Artificial intelligence holds.

1. High Costs

The ability to create a machine that can simulate human intelligence is no small feat. It requires plenty of time and resources and can cost a huge deal of money. AI also needs to operate on the latest hardware and software to stay updated and meet the latest requirements, thus making it quite costly.

2. No Creativity

A big disadvantage of AI is that it cannot learn to think outside the box. AI is capable of learning over time with pre-fed data and past experiences, but cannot be creative in its approach. A classic example is the bot Quill who can write Forbes earning reports . These reports only contain data and facts already provided to the bot. Although it is impressive that a bot can write an article on its own, it lacks the human touch present in other Forbes articles. 

3. Unemployment

One application of artificial intelligence is a robot, which is displacing occupations and increasing unemployment (in a few cases). Therefore, some claim that there is always a chance of unemployment as a result of chatbots and robots replacing humans. 

For instance, robots are frequently utilized to replace human resources in manufacturing businesses in some more technologically advanced nations like Japan. This is not always the case, though, as it creates additional opportunities for humans to work while also replacing humans in order to increase efficiency.

4. Make Humans Lazy

AI applications automate the majority of tedious and repetitive tasks. Since we do not have to memorize things or solve puzzles to get the job done, we tend to use our brains less and less. This addiction to AI can cause problems to future generations.

5. No Ethics

Ethics and morality are important human features that can be difficult to incorporate into an AI. The rapid progress of AI has raised a number of concerns that one day, AI will grow uncontrollably, and eventually wipe out humanity. This moment is referred to as the AI singularity.

6. Emotionless

Since early childhood, we have been taught that neither computers nor other machines have feelings. Humans function as a team, and team management is essential for achieving goals. However, there is no denying that robots are superior to humans when functioning effectively, but it is also true that human connections, which form the basis of teams, cannot be replaced by computers.

7. No Improvement

Humans cannot develop artificial intelligence because it is a technology based on pre-loaded facts and experience. AI is proficient at repeatedly carrying out the same task, but if we want any adjustments or improvements, we must manually alter the codes. AI cannot be accessed and utilized akin to human intelligence, but it can store infinite data.

Machines can only complete tasks they have been developed or programmed for; if they are asked to complete anything else, they frequently fail or provide useless results, which can have significant negative effects. Thus, we are unable to make anything conventional.

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Now that you know both the pros and cons of Artificial Intelligence, one thing is for sure has massive potential for creating a better world to live in. The most important role for humans will be to ensure that the rise of the AI doesn’t get out of hand. Although there are both debatable pros and cons of artificial intelligence , its impact on the global industry is undeniable. It continues to grow every single day driving sustainability for businesses. This certainly calls for the need of AI literacy and upskilling to prosper in many new age jobs. Simplilearn’s Caltech Post Graduate Program in AI & ML will help you fast track your career in AI and prepare you for one of the world’s most exciting jobs. This program covers both AI basics and advanced topics such as deep learning networks , NLP, and reinforcement learning. Get started with this course today and build your dream career in AI.

1. What are the benefits of Artificial Intelligence (AI)?

• Increased Efficiency: AI can automate repetitive tasks, improving efficiency and productivity in various industries.
• Data Analysis and Insights: AI algorithms can analyze large data quickly, providing valuable insights for decision-making.
• 24/7 Availability: AI-powered systems can operate continuously, offering round-the-clock services and support.
• Improved Accuracy: AI can perform tasks with high precision, reducing errors and improving overall accuracy.
• Personalization: AI enables personalized experiences and recommendations based on individual preferences and behavior.
• Safety and Risk Reduction: AI can be used for tasks that are hazardous to humans, reducing risks and ensuring safety.

2. What are the disadvantages of Artificial Intelligence (AI)?

• Job Displacement: AI automation may lead to job losses in certain industries, affecting the job market and workforce.
• Ethical Concerns: AI raises ethical issues, including data privacy, algorithm bias, and potential misuse of AI technologies.
• Lack of Creativity and Empathy: AI lacks human qualities like creativity and empathy, limiting its ability to understand emotions or produce original ideas.
• Cost and Complexity: Developing and implementing AI systems can be expensive, require specialized knowledge and resources.
• Reliability and Trust: AI systems may not always be fully reliable, leading to distrust in their decision-making capabilities.
• Dependency on Technology: Over-reliance on AI can make humans dependent on technology and reduce critical thinking skills.

3. How can businesses benefit from adopting AI? 

Businesses can benefit from adopting AI in various ways, such as:

• Streamlining operations and reducing operational costs.
• Enhancing customer experiences through personalized services and support.
• Optimizing supply chain management and inventory control.
• Predictive analytics for better decision-making and market insights.
• Improve product and service offerings based on customer feedback and data analysis.

4. What are some AI applications in everyday life? 

AI applications in everyday life include:

• Virtual assistants like Siri and Alexa, which help with voice commands and information retrieval.
• Social media algorithms that curate personalized content for users.
• Recommendation systems on streaming platforms, suggesting movies and shows based on viewing history.
•  Financial institutions use fraud detection systems to identify suspicious transactions.
• AI-powered healthcare diagnostics for disease detection and treatment planning.

5. What are the advantages of AI in education?

• Personalized learning: AI has the capability to analyze individual student data, enabling the provision of personalized learning experiences that cater to each student's needs and preferred learning styles. As a result, students can progress at their own pace and receive the necessary assistance for their academic success.
• Improved engagement and motivation: AI can create more interactive and engaging learning experiences that can help students stay focused in their learning.
• Enhanced assessment and feedback: AI can provide more accurate and timely assessment and feedback to help students track their progress and identify areas where they need additional support.
• Increased access to education: AI can help to increase access to education by providing more personalized and affordable learning opportunities.
• Improved teacher training: AI can help to improve teacher training by providing teachers with data and insights that can help them better understand their students and their needs.

6. How does Artificial Intelligence reduce costs?  

AI can reduce costs by automating repetitive tasks, increasing efficiency, and minimizing errors. This leads to improved productivity and resource allocation, ultimately resulting in cost savings.

7. Can AI replace human intelligence and creativity?

 While AI can perform specific tasks with remarkable precision, it cannot fully replicate human intelligence and creativity. AI lacks consciousness and emotions, limiting its ability to understand complex human experiences and produce truly creative works.

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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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The profound impact of Artificial Intelligence on society – Exploring the far-reaching implications of AI technology

Artificial intelligence (AI) has revolutionized the way we live and work, and its influence on society continues to grow. This essay explores the impact of AI on various aspects of our lives, including economy, employment, healthcare, and even creativity.

One of the most significant impacts of AI is on the economy. AI-powered systems have the potential to streamline and automate various processes, increasing efficiency and productivity. This can lead to economic growth and increased competitiveness in the global market. However, it also raises concerns about job displacement and income inequality, as AI technologies replace certain job roles.

In the realm of healthcare, AI has already made its mark. From early detection of diseases to personalized treatment plans, AI algorithms have become invaluable in improving patient outcomes. With the ability to analyze vast amounts of medical data, AI systems can identify patterns and make predictions that human doctors may miss. Nevertheless, ethical considerations regarding patient privacy and data security need to be addressed.

Furthermore, AI’s impact on creativity is an area of ongoing exploration. While AI technologies can generate artwork, music, and literature, the question of whether they can truly replicate human creativity remains. Some argue that AI can enhance human creativity by providing new tools and inspiration, while others fear that it may diminish the value of genuine human artistic expression.

In conclusion, the impact of artificial intelligence on society is multifaceted. While it brings economic advancements and improvements in healthcare, it also presents challenges and ethical dilemmas. As AI continues to evolve, it is crucial to strike a balance that maximizes its benefits while minimizing its potential drawbacks.

The Definition of Artificial Intelligence

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It involves the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and problem-solving.

AI has a profound impact on society, revolutionizing various industries and sectors. Its disruptive nature has led to significant advancements in the way businesses operate, healthcare is delivered, and everyday tasks are performed. AI technologies have the potential to automate repetitive tasks, analyze vast amounts of data with speed and accuracy, and enhance the efficiency and effectiveness of various processes.

Furthermore, AI has the potential to transform the workforce, leading to changes in the job market. While some fear that AI will replace human workers and result in unemployment, others argue that it will create new job opportunities and improve overall productivity. The societal impact of AI is complex and multifaceted, necessitating careful consideration and management.

In summary , artificial intelligence is the development of computer systems that can mimic human intelligence and perform tasks that traditionally require human thinking. Its impact on society is vast, affecting industries, job markets, and everyday life. Understanding the definition and implications of AI is crucial as we navigate the ever-evolving technological landscape.

The History of Artificial Intelligence

The impact of artificial intelligence on society is a topic that has gained increasing attention in recent years. As technology continues to advance at a rapid pace, the capabilities of artificial intelligence are expanding as well. But how did we get to this point? Let’s take a brief look at the history of artificial intelligence.

The concept of artificial intelligence dates back to ancient times, with the development of mechanical devices that were capable of performing simple calculations. However, it wasn’t until the mid-20th century that the field of AI began to take shape.

In 1956, a group of researchers organized the famous Dartmouth Conference, where the field of AI was officially born. This conference brought together leading experts from various disciplines to explore the possibilities of creating “machines that can think.”

During the following decades, AI research progressed with the development of first-generation computers and the introduction of programming languages. In the 1960s, researchers focused on creating natural language processing systems, while in the 1970s, expert systems became popular.

However, in the 1980s, AI faced a major setback known as the “AI winter.” Funding for AI research significantly declined due to the lack of significant breakthroughs. The field faced criticism and skepticism, and it seemed that the promise of AI might never be realized.

But in the 1990s, AI began to emerge from its winter. The introduction of powerful computers and the availability of massive amounts of data fueled the development of machine learning algorithms. This led to significant advancements in areas such as computer vision, speech recognition, and natural language processing.

Over the past few decades, AI has continued to evolve and impact various aspects of society. From virtual assistants like Siri and Alexa to autonomous vehicles and recommendation systems, artificial intelligence is becoming increasingly integrated into our daily lives.

As we move forward, the impact of artificial intelligence on society is only expected to grow. With ongoing advancements in AI technology, we can expect to see even more significant changes in fields such as healthcare, finance, transportation, and more.

In conclusion, the history of artificial intelligence is one of perseverance and innovation. From its humble beginnings to its current state, AI has come a long way. It has evolved from simple mechanical devices to complex algorithms that can learn and make decisions. The impact of artificial intelligence on society will continue to shape our future, and it is essential to consider both the positive and negative implications as we navigate this technological revolution.

The Advantages of Artificial Intelligence

Artificial intelligence (AI) is a rapidly developing technology that is having a significant impact on society. It has the potential to revolutionize various aspects of our lives, bringing about many advantages that can benefit individuals and communities alike.

1. Increased Efficiency

One of the major advantages of AI is its ability to automate tasks and processes, leading to increased efficiency. AI systems can analyze large amounts of data and perform complex calculations at a speed much faster than humans. This can help businesses optimize their operations, reduce costs, and improve productivity.

2. Enhanced Accuracy

AI technologies can also improve accuracy and precision in various domains. Machine learning algorithms can learn from large datasets and make predictions or decisions with a high level of accuracy. This can be particularly beneficial in fields such as healthcare, where AI can assist doctors in diagnosing diseases, detecting patterns in medical images, and recommending personalized treatments.

Additionally, AI-powered systems can minimize human error in areas where precision is crucial, such as manufacturing and transportation. By automating repetitive tasks and monitoring processes in real-time, AI can help avoid costly mistakes and improve overall quality.

Overall, the advantages of artificial intelligence are numerous and diverse. From increased efficiency to enhanced accuracy, AI has the potential to transform various industries and improve the quality of life for individuals and societies as a whole. It is crucial, however, to continue exploring the ethical implications of AI and ensure that its development is guided by principles that prioritize the well-being and safety of humanity.

The Disadvantages of Artificial Intelligence

While the impact of artificial intelligence on society has been largely positive, it is important to also consider its disadvantages.

1. Job Displacement

One of the biggest concerns regarding artificial intelligence is the potential for job displacement. As machines become more intelligent and capable of performing complex tasks, there is a growing fear that many jobs will become obsolete. This can lead to unemployment and economic instability, as individuals struggle to find work in a society increasingly dominated by artificial intelligence.

2. Ethical Concerns

Another disadvantage of artificial intelligence is the ethical concerns it raises. As artificial intelligence systems become more advanced, there is a need for clear guidelines and regulations to ensure that they are used responsibly. Issues such as privacy, data protection, and algorithmic bias need to be addressed to prevent misuse or unintended consequences.

In conclusion, while artificial intelligence has had a positive impact on society, there are also disadvantages that need to be considered. Job displacement and ethical concerns are just a few of the challenges that need to be addressed as we continue to advance in the field of artificial intelligence.

The Ethical Concerns of Artificial Intelligence

As artificial intelligence continues to impact society in numerous ways, it is important to address the ethical concerns that arise from its use. As AI becomes more commonplace in various industries, including healthcare, finance, and transportation, the potential for unintended consequences and ethical dilemmas increases.

One of the primary ethical concerns of artificial intelligence is the issue of privacy. With the advancements in AI technology, there is a growing ability for machines to collect and analyze vast amounts of personal data. This raises questions about how this data is used, who has access to it, and whether individuals have a right to control and protect their own information.

Another ethical concern is the potential for AI to perpetuate and amplify existing biases and discrimination. AI algorithms are trained on existing data, which can reflect societal biases and prejudices. If these biases are not identified and addressed, AI systems can inadvertently perpetuate unfair practices and discrimination, leading to negative impacts on marginalized communities.

Additionally, the use of AI in decision-making processes raises concerns about accountability and transparency. As AI systems make more complex decisions that affect individuals’ lives, it becomes crucial to understand how these decisions are made. Lack of transparency and accountability can result in a loss of trust in AI systems, especially if they make decisions that have significant consequences.

Furthermore, there is the concern of the impact of AI on employment and the workforce. As AI technology advances, there is the potential for job displacement and the loss of livelihoods. This raises questions about the responsibility of society to provide support and retraining for individuals who are affected by the automation of tasks previously carried out by humans.

Overall, as artificial intelligence continues to evolve and become more integrated into society, it is crucial to actively address the ethical concerns that arise. This involves establishing clear guidelines and regulations to safeguard privacy, address biases, ensure transparency, and mitigate the impact on employment. By addressing these concerns proactively, society can harness the benefits of AI while minimizing its negative impacts.

The Impact of Artificial Intelligence on Jobs

The advancement of artificial intelligence (AI) technology is having a profound impact on society as a whole. One area that is particularly affected by this technological revolution is the job market. The introduction of AI into various industries is changing the way we work and the types of jobs that are available. It is important to understand the implications of this impact on jobs and how it will shape the future of work.

The Rise of Automation

One of the main ways AI impacts jobs is through automation. AI algorithms and machines are increasingly replacing human workers in repetitive and routine tasks. Jobs that involve tasks that can be easily automated, such as data entry or assembly line work, are being taken over by AI-powered technology. This shift towards automation has the potential to lead to job displacement and unemployment for many individuals.

New Opportunities and Skill Requirements

While AI may be replacing certain jobs, it is also creating new opportunities. As industries become more automated, there is a growing demand for workers who are skilled in managing and developing AI technology. Jobs that require expertise in AI programming and data analysis are becoming increasingly important. This means that individuals who possess these skills will have an advantage in the job market, while those without them may struggle to find employment.

Furthermore, AI technology has the potential to transform existing jobs rather than eliminate them entirely. As AI systems become more sophisticated, they can assist human workers in performing tasks more efficiently and accurately. This collaboration between humans and machines can lead to increased productivity and job growth in certain industries.

The Need for Adaptation and Lifelong Learning

The impact of AI on jobs highlights the importance of adaptation and lifelong learning. As technology continues to evolve, workers must be willing to learn new skills and adapt to changing job requirements. The ability to continuously update one’s skills will be crucial in order to remain relevant in the job market. This necessitates a shift towards lifelong learning and a willingness to embrace new technologies.

In conclusion, the impact of artificial intelligence on jobs is significant and multifaceted. While AI technology has the potential to automate certain tasks and lead to job displacement, it also creates new opportunities and changes the nature of existing jobs. The key to navigating this changing job market is adaptation, lifelong learning, and acquiring new skills in AI-related fields. By understanding and adapting to the impact of AI on jobs, society can ensure that the benefits of this technology are maximized while minimizing negative consequences.

The Impact of Artificial Intelligence on Education

Artificial intelligence (AI) is rapidly transforming various aspects of society, and one area where its impact is particularly noteworthy is education. In this essay, we will explore how AI is revolutionizing the educational landscape and the implications it has for both teachers and students.

AI has the potential to greatly enhance the learning experience for students. With intelligent algorithms and personalized learning platforms, students can receive customized instruction tailored to their individual needs and learning styles. This can help to bridge gaps in understanding, improve retention, and ultimately lead to better academic outcomes.

Moreover, AI can serve as a valuable tool for teachers. By automating administrative tasks, such as grading and data analysis, teachers can save time and focus on what they do best: teaching. AI can also provide valuable insights into student performance and progress, allowing teachers to identify areas where additional support may be needed.

However, it is important to recognize that AI is not a substitute for human teachers. While AI can provide personalized instruction and automate certain tasks, it lacks the emotional intelligence and interpersonal skills that are essential for effective teaching. Teachers play a critical role in creating a supportive and nurturing learning environment, and their expertise cannot be replaced by technology.

Another concern is the potential bias and ethical implications associated with AI in education. With algorithms determining the content and delivery of educational materials, there is a risk of reinforcing existing inequalities and perpetuating discriminatory practices. It is crucial to ensure that AI systems are designed and implemented in an ethical and inclusive manner, taking into account issues of fairness and equity.

In conclusion, the impact of artificial intelligence on education is profound. It has the potential to revolutionize the way students learn and teachers teach. However, it is crucial to approach AI in education with caution, being mindful of the limitations and ethical considerations. By harnessing the power of AI while preserving the irreplaceable role of human teachers, we can create a future of education that is truly transformative.

The Impact of Artificial Intelligence on Healthcare

Artificial intelligence (AI) is revolutionizing the healthcare industry, and its impact on society cannot be overstated. Through the use of advanced algorithms and machine learning, AI is transforming various aspects of healthcare, from diagnosis and treatment to drug discovery and patient care.

One of the key areas where AI is making a significant impact is in diagnosing diseases. With the ability to analyze massive amounts of medical data, AI algorithms can now detect patterns and identify potential diseases in patients more accurately and efficiently than ever before. This can lead to early detection and intervention, ultimately saving lives.

AI is also streamlining the drug discovery process, which traditionally has been a time-consuming and costly endeavor. By analyzing vast amounts of data and simulating molecular structures, AI can help researchers identify potential drug candidates more quickly and accurately. This has the potential to accelerate the development of new treatments and improve patient outcomes.

Furthermore, AI is transforming patient care through personalized medicine. By analyzing an individual’s genetic and medical data, AI algorithms can provide personalized treatment plans tailored to the specific needs of each patient. This can lead to more effective treatments, reduced side effects, and improved overall patient satisfaction.

In addition to diagnosis and treatment, AI is also improving healthcare delivery and efficiency. AI-powered chatbots and virtual assistants can now provide patients with personalized medical advice and answer their questions 24/7. This reduces the burden on healthcare providers and allows for more accessible and convenient healthcare services.

However, as with any new technology, there are also challenges and concerns surrounding the use of AI in healthcare. Issues such as data privacy, ethical considerations, and bias in algorithms need to be addressed to ensure that AI is used responsibly and for the benefit of all patients.

In conclusion, the impact of artificial intelligence on healthcare is immense. With advancements in AI, the healthcare industry is poised to revolutionize patient care, diagnosis, and treatment. However, it is crucial to address the ethical and privacy concerns associated with AI to ensure that it is used responsibly and for the greater good of society.

The Impact of Artificial Intelligence on Transportation

Artificial intelligence (AI) has had a significant impact on society in many different areas, and one of the fields that has benefited greatly from AI technology is transportation. With advances in AI, transportation systems have become more efficient, safer, and more environmentally friendly.

Improved Safety

One of the key impacts of AI on transportation is the improved safety of both passengers and drivers. AI technology has enabled the development of autonomous vehicles, which can operate without human intervention. These vehicles use AI algorithms and sensors to navigate roads, avoiding accidents and minimizing collisions. By removing the human element from driving, the risk of human error and accidents caused by fatigue, distraction, or impaired judgment can be significantly reduced.

Efficient Traffic Management

AI has also revolutionized traffic management systems, leading to more efficient transportation networks. Intelligent traffic lights, for example, can use AI algorithms to adjust signal timings based on real-time traffic conditions, optimizing traffic flow and reducing congestion. AI-powered algorithms can analyze large amounts of data from various sources, such as traffic cameras and sensors, to provide accurate predictions and recommendations for traffic management and planning.

Enhanced Logistics and Delivery

AI has significantly impacted the logistics and delivery industry. AI-powered software can optimize route planning for delivery vehicles, taking into account factors such as traffic conditions, weather, and delivery time windows. This improves efficiency and reduces costs by minimizing fuel consumption and maximizing the number of deliveries per trip. Additionally, AI can also assist in package sorting and tracking, enhancing the overall speed and accuracy of the delivery process.

The impact of AI on transportation is continuously evolving, with ongoing research and development leading to even more advanced applications. As AI technology continues to improve, we can expect transportation systems to become even safer, more efficient, and more sustainable.

The Impact of Artificial Intelligence on Communication

Artificial intelligence has had a profound impact on society, affecting various aspects of our lives. One area where its influence can be seen is in communication. The advancements in artificial intelligence have revolutionized the way we communicate with each other.

One of the main impacts of artificial intelligence on communication is the development of chatbots. These computer programs are designed to simulate human conversation and interact with users through messaging systems. Chatbots have become increasingly popular in customer service, providing quick and automated responses to customer inquiries. They are available 24/7, ensuring constant support and improving customer satisfaction.

Moreover, artificial intelligence has contributed to the improvement of language translation. Translation tools powered by AI technology have made it easier for people to communicate across languages and cultures. These tools can instantly translate text and speech, enabling effective communication in real-time. They have bridged the language barrier and facilitated global collaboration and understanding.

Another impact of artificial intelligence on communication is the emergence of voice assistants. These virtual assistants, such as Siri and Alexa, use natural language processing and machine learning algorithms to understand and respond to user commands. Voice assistants have become integral parts of our daily lives, helping us perform various tasks, from setting reminders to controlling smart home devices. They have transformed the way we interact with technology and simplified communication with devices.

Artificial intelligence has also played a role in enhancing communication through personalized recommendations. Many online platforms, such as social media and streaming services, utilize AI algorithms to analyze user preferences and provide personalized content suggestions. This has improved user engagement and facilitated communication by connecting users with relevant information and like-minded individuals.

In conclusion, artificial intelligence has had a significant impact on communication. From chatbots and language translation to voice assistants and personalized recommendations, AI technology has revolutionized the way we interact and communicate with each other. It has made communication faster, more efficient, and more accessible, bringing people closer together in an increasingly interconnected world.

The Impact of Artificial Intelligence on Privacy

Artificial intelligence (AI) has had a profound impact on various aspects of our society, and one area that is greatly affected is privacy. With the advancements in AI technology, there are growing concerns about how it can impact our privacy rights.

AI-powered systems have the ability to collect and analyze vast amounts of personal data, ranging from social media activity to online transactions. This presents significant challenges when it comes to protecting our privacy. For instance, AI algorithms can mine and analyze our personal data to generate targeted advertisements, which can result in intrusion into our personal lives.

Additionally, AI systems can be used to monitor and track individuals’ online activities, which raises concerns about surveillance and the erosion of privacy. With AI’s ability to process and interpret large volumes of data, it becomes easier for organizations and governments to gather information about individuals without their knowledge or consent.

Furthermore, AI algorithms can make predictions about individuals’ behaviors and preferences based on their data. While this can be beneficial in some cases, such as providing tailored recommendations, it also raises concerns about the potential misuse of this information. For example, insurance companies could use AI algorithms to assess an individual’s health risks based on their online activity, resulting in potential discrimination or exclusion.

It is crucial to strike a balance between the benefits of AI technology and protecting individuals’ right to privacy. Steps must be taken to ensure that AI systems are designed and implemented in a way that respects and safeguards privacy. This can include implementing strict regulations and guidelines for data collection, storage, and usage.

In conclusion, the impact of artificial intelligence on privacy cannot be ignored. As AI continues to advance, it is essential to address the potential risks and challenges it poses to privacy rights. By taking proactive measures and promoting ethical practices, we can harness the benefits of AI while ensuring that individuals’ privacy is respected and protected.

The Impact of Artificial Intelligence on Security

Artificial intelligence (AI) has had a profound impact on society, and one area where its influence is particularly noticeable is in the field of security. The development and implementation of AI technology have revolutionized the way we approach and manage security threats.

AI-powered security systems have proven to be highly effective in detecting and preventing various types of threats, such as cyber attacks, terrorism, and physical breaches. These systems are capable of analyzing vast amounts of data in real-time, identifying patterns, and recognizing anomalies that may indicate a security risk.

One major advantage of AI in security is its ability to continuously adapt and learn. AI algorithms can quickly analyze new data and update their knowledge base, improving their ability to detect and respond to emerging threats. This dynamic nature allows AI-powered security systems to stay ahead of potential attackers and respond to evolving security challenges.

Furthermore, AI can enhance the efficiency and accuracy of security operations. By automating certain tasks, such as video surveillance monitoring and threat analysis, AI technology can significantly reduce the workload for human security personnel. This frees up resources and enables security teams to focus on more critical tasks, such as responding to incidents and developing proactive security strategies.

However, the increasing reliance on AI in security also raises concerns. The use of AI technology can potentially lead to privacy breaches and unethical surveillance practices. It is crucial to strike a balance between utilizing AI for security purposes and respecting individual privacy rights.

In conclusion, the impact of artificial intelligence on security has been significant. AI-powered systems have revolutionized the way we detect and prevent security threats, enhancing efficiency and accuracy in security operations. However, ethical concerns need to be addressed to ensure that AI is used responsibly and in a way that respects individual rights and privacy.

The Impact of Artificial Intelligence on Economy

Artificial intelligence (AI) is revolutionizing the economy in various ways. Its impact is prevalent across different sectors, leading to both opportunities and challenges.

One of the key benefits of AI in the economy is increased productivity. AI-powered systems and algorithms can perform tasks at a much faster pace and with a higher level of accuracy compared to humans. This efficiency can lead to significant cost savings for businesses and result in increased output and profits.

Moreover, AI has the potential to create new job opportunities. While some jobs may be replaced by automation, AI also leads to the creation of new roles that require specialized skills in managing and maintaining AI systems. This can contribute to economic growth and provide employment opportunities for individuals with the necessary technical expertise.

The impact of AI on the economy is not limited to individual businesses or sectors. It has the potential to transform entire industries. For example, AI-powered technologies can optimize supply chain operations, enhance customer experience, and improve decision-making processes. These advancements can lead to increased competitiveness, improved efficiency, and overall economic growth.

However, the widespread implementation of AI also brings challenges. The displacement of jobs due to automation can result in unemployment and income inequality. It is crucial for policymakers to address these issues and ensure that the benefits of AI are distributed equitably across society.

Additionally, the ethical implications of AI in the economy must be considered. As AI systems continue to advance, it raises questions about privacy, data security, and algorithmic bias. Safeguards and regulations need to be in place to protect individuals’ rights and prevent any potential harm caused by AI applications.

In conclusion, the impact of artificial intelligence on the economy is significant. It offers opportunities for increased productivity, job creation, and industry transformation. However, it also poses challenges such as job displacement and ethical concerns. To fully harness the potential of AI in the economy, policymakers and stakeholders must work together to address these challenges and ensure a balanced and inclusive approach to its implementation.

The Impact of Artificial Intelligence on Entertainment

Artificial intelligence is revolutionizing the entertainment industry, transforming the way we consume and experience various forms of media. With its ability to analyze massive amounts of data, AI has the potential to enhance entertainment in numerous ways.

One area where AI is making a significant impact is in content creation. AI algorithms can generate music, art, and even scripts for movies and TV shows. By analyzing patterns and trends in existing content, AI can create new and original pieces that appeal to different audiences. This not only increases the diversity of entertainment options but also reduces the time and effort required for human creators.

AI also plays a crucial role in enhancing the user experience in the entertainment industry. For example, AI-powered recommendation engines can suggest relevant movies, TV shows, or songs based on individual preferences and viewing habits. This personalized approach ensures that users discover content that aligns with their interests, leading to a more enjoyable and engaging entertainment experience.

In the gaming industry, AI is transforming the way games are developed and played. AI algorithms can create lifelike characters and virtual worlds, providing players with immersive and realistic experiences. Additionally, AI-powered game assistants can adapt to the player’s skill level and offer personalized guidance, making games more accessible and enjoyable for players of all abilities.

Furthermore, AI is revolutionizing the way we consume live events, such as sports or concerts. AI-powered cameras and sensors can capture and analyze data in real-time, providing enhanced viewing experiences for spectators. This includes features like instant replays, personalized camera angles, and in-depth statistics. AI can also generate virtual crowds or even simulate the experience of attending a live event, bringing the excitement of the event to a global audience.

The impact of artificial intelligence on the entertainment industry is undeniable. It is transforming content creation, enhancing the user experience, and revolutionizing the way we consume various forms of media. As AI continues to advance, we can expect even more innovative and immersive entertainment experiences that cater to individual preferences and push the boundaries of creativity.

The Impact of Artificial Intelligence on Human Interaction

In today’s modern world, the rise of artificial intelligence (AI) has had a profound impact on many aspects of society, including human interaction. AI technology has revolutionized the way we communicate and interact with one another, both online and offline.

One of the most noticeable impacts of AI on human interaction is in the realm of communication. AI-powered chatbots and virtual assistants have become increasingly common, allowing people to interact with machines in a more natural and intuitive way. Whether it’s using voice commands to control smart home devices or chatting with a virtual assistant to get information, AI has made it easier to communicate with technology.

AI has also had a significant impact on social media and online communication platforms. Social media algorithms use AI to analyze user data and tailor content to individual preferences, which can shape the way we interact with each other online. This can lead to both positive and negative effects, as AI algorithms may reinforce existing beliefs and create echo chambers, but they can also expose us to new ideas and perspectives.

Furthermore, AI technology has the potential to enhance human interaction by augmenting our capabilities. For example, AI-powered translation tools can break down language barriers and facilitate communication between people who speak different languages. This can foster cross-cultural understanding and enable collaboration on a global scale.

On the other hand, there are concerns about the potential negative impact of AI on human interaction. Some argue that the increasing reliance on AI technology for communication could lead to a decline in human social skills. As people become more accustomed to interacting with machines, they may struggle to engage in authentic face-to-face interactions.

Despite these concerns, it is clear that AI has had a profound impact on human interaction. From enhancing communication to breaking down language barriers, AI technology has transformed the way we interact with one another. It is crucial to continue monitoring and studying the impact of AI on human interaction to ensure we strike a balance between technological advancement and preserving our social connections.

The Role of Artificial Intelligence in Scientific Research

Artificial intelligence (AI) has had a significant impact on society in various fields, and one area where it has shown great promise is scientific research. The use of AI in scientific research has revolutionized the way experiments are conducted, data is analyzed, and conclusions are drawn.

Improving Experimental Design and Data Collection

One of the key contributions of AI in scientific research is its ability to improve experimental design and data collection. By utilizing machine learning algorithms, AI systems can analyze massive amounts of data and identify patterns, allowing researchers to optimize their experimental approaches and make more informed decisions. This not only saves time and resources but also increases the accuracy and reliability of scientific findings.

Enhancing Data Analysis and Interpretation

Another crucial role of AI in scientific research is its ability to enhance data analysis and interpretation. Traditional data analysis methods can be time-consuming and subjective, leading to potential biases. However, AI systems can process vast amounts of data quickly and objectively, revealing hidden relationships, trends, and insights that may be missed by human researchers. This enables scientists to extract meaningful information from complex datasets, leading to more accurate and comprehensive conclusions.

While AI has significant potential in scientific research, it also presents challenges and ethical considerations that need to be addressed. Privacy and security concerns, biases in AI algorithms, ethical implications of AI decision-making, and the impact on human researchers’ roles are some of the critical issues that require scrutiny.

In conclusion, the role of artificial intelligence in scientific research is undeniable. AI has the potential to revolutionize how experiments are designed, data is analyzed, and conclusions are drawn. By improving experimental design and data collection, enhancing data analysis and interpretation, and accelerating scientific discovery, AI can significantly contribute to the advancement of scientific knowledge and its impact on society as a whole.

The Role of Artificial Intelligence in Space Exploration

Artificial intelligence (AI) has had a significant impact on various fields and industries, and space exploration is no exception. With its ability to analyze vast amounts of data and make decisions quickly, AI has revolutionized the way we explore space and gather information about the universe.

One of the primary roles of artificial intelligence in space exploration is in the analysis of data collected by space probes and telescopes. These devices capture enormous amounts of data that can often be overwhelming for human scientists to process. AI algorithms can sift through this data, identifying patterns, and extracting valuable insights that humans may not have noticed.

Additionally, AI plays a crucial role in autonomous navigation and spacecraft control. Spacecraft can be sent to explore distant planets and moons in our solar system, and AI-powered systems can ensure their safe and efficient navigation through unknown terrain. AI algorithms can analyze data from onboard sensors and make real-time decisions to avoid obstacles and hazards.

Benefits of AI in space exploration

• Efficiency: AI systems can process vast amounts of data much faster than humans, allowing for quicker analysis and decision-making.
• Exploration of inhospitable environments: AI-powered robots can be sent to explore extreme environments, such as the surface of Mars or the icy moons of Jupiter, where it would be challenging for humans to survive.
• Cost reduction: By using AI to automate certain tasks, space exploration missions can become more cost-effective and efficient.

The impact of artificial intelligence on space exploration is still in its early stages, but its potential is vast. As AI technology continues to advance, we can expect to see even more significant contributions to our understanding of the universe and our ability to explore it.

The Role of Artificial Intelligence in Environmental Conservation

Artificial intelligence (AI) has the potential to revolutionize various aspects of society, and environmental conservation is no exception. With the growing concern about climate change and the need to preserve the planet’s resources, AI can play a crucial role in helping us address these challenges.

Monitoring and Predicting Environmental Changes

One of the key benefits of AI in environmental conservation is its ability to monitor and predict environmental changes. Through the use of sensors and data analysis, AI systems can gather and analyze vast amounts of information about the environment, including temperature, air quality, and water levels.

This data can then be used to identify patterns and trends, allowing scientists to make predictions about future changes. For example, AI can help predict the spread of wildfires or the impact of deforestation in certain areas. By understanding these threats in advance, we can take proactive measures to protect our natural resources.

Optimizing Resource Management

Another important role of AI in environmental conservation is optimizing resource management. By using AI algorithms, we can efficiently allocate resources such as energy, water, and waste management.

AI can analyze data from various sources, such as smart meters and sensors, to understand patterns of resource usage. This information can then be used to develop strategies for more sustainable resource management, reducing waste and improving efficiency.

For example, AI can help optimize energy consumption in buildings by analyzing data from smart thermostats and occupancy sensors. It can identify usage patterns and make adjustments to reduce energy waste, saving both money and environmental resources.

Supporting Conservation Efforts

AI can also support conservation efforts through various applications. One example is the use of AI-powered drones and satellite imagery to monitor and protect endangered species.

By analyzing images and data collected by these technologies, AI algorithms can identify and track animals, detect illegal activities such as poaching, and even help with habitat restoration. This technology can greatly enhance the effectiveness and efficiency of conservation efforts, allowing us to better protect our biodiversity.

In conclusion, artificial intelligence has a significant role to play in environmental conservation. From monitoring and predicting environmental changes to optimizing resource management and supporting conservation efforts, AI can provide valuable insights and help us make more informed decisions. By harnessing the power of AI, we can work towards a more sustainable and environmentally conscious society.

The Role of Artificial Intelligence in Manufacturing

Artificial intelligence (AI) has had a profound impact on society in various fields, and manufacturing is no exception. In this essay, we will explore the role of AI in manufacturing and how it has revolutionized the industry.

AI has transformed the manufacturing process by introducing automation and machine learning techniques. With AI, machines can perform tasks that were previously done by humans, leading to increased efficiency and productivity. This has allowed manufacturers to streamline their operations and produce goods at a faster rate.

One of the key benefits of AI in manufacturing is its ability to analyze large amounts of data. Through machine learning algorithms, AI systems can collect and process data from various sources, such as sensors and machines, to identify patterns and make informed decisions. This allows manufacturers to optimize their production processes and minimize errors.

Furthermore, AI can improve product quality and reduce defects. By analyzing data in real-time, AI systems can detect anomalies and deviations from the norm, allowing manufacturers to identify and address issues before they escalate. This not only saves time and costs but also ensures that consumers receive high-quality products.

Additionally, AI has enabled the development of predictive maintenance systems. By analyzing data from machines and equipment, AI can anticipate and prevent failures before they occur. This proactive approach minimizes downtime, reduces maintenance costs, and extends the lifespan of machinery.

Overall, the role of AI in manufacturing is transformative. It empowers manufacturers to optimize their processes, improve product quality, and reduce costs. However, it is important to note that AI is not a replacement for humans in the manufacturing industry. Instead, it complements human skills and expertise, allowing workers to focus on more complex tasks while AI handles repetitive and mundane tasks.

In conclusion, artificial intelligence has had a significant impact on the manufacturing industry. It has revolutionized processes, improved product quality, and increased productivity. As AI continues to advance, we can expect even more transformative changes in the manufacturing sector.

The Role of Artificial Intelligence in Agriculture

Artificial intelligence has had a profound impact on society in various fields, and agriculture is no exception. With the advancements in technology, AI has the potential to revolutionize the agricultural industry, making it more efficient, sustainable, and productive.

One of the key areas where AI can play a significant role in agriculture is in crop management. AI-powered systems can analyze vast amounts of data, such as weather patterns, soil conditions, and crop health, to provide farmers with valuable insights. This allows farmers to make more informed decisions on irrigation, fertilization, and pest control, leading to optimal crop yields and reduced resource waste.

Moreover, AI can also aid in the early detection and prevention of crop diseases. By using machine learning algorithms, AI systems can identify patterns and anomalies in plant health, indicating the presence of diseases or pests. This enables farmers to take timely action, prevent the spread of diseases, and minimize crop losses.

Another area where AI can contribute to agriculture is in the realm of precision farming. By combining AI with other technologies like drones and sensors, farmers can gather precise and real-time data about their crops and fields. This data can then be used to create detailed maps, monitor crop growth, and optimize resource allocation. Whether it’s optimizing water usage or determining the ideal time for harvesting, AI can help farmers make data-driven decisions that maximize productivity while minimizing environmental impact.

Furthermore, AI can enhance livestock management. With AI-powered systems, farmers can monitor the health and behavior of their livestock, detect diseases or anomalies, and provide personalized care. This not only improves animal welfare but also increases the efficiency of livestock production.

In conclusion, artificial intelligence has a crucial role to play in the agricultural sector. From crop management to livestock monitoring, AI can bring numerous benefits to farmers, leading to increased productivity, sustainability, and overall growth. As AI continues to advance, we can expect further innovations and improvements in the integration of AI in agriculture, shaping the future of food production.

The Role of Artificial Intelligence in Finance

Artificial intelligence (AI) has had a significant impact on society, revolutionizing various industries, and finance is no exception. In this essay, we will explore the role of AI in the financial sector and its implications.

The use of AI has transformed numerous aspects of finance, from trading and investment to risk management and fraud detection. One of the key benefits of AI in finance is its ability to process vast amounts of data in real-time. This enables more accurate predictions and informed decision-making, giving financial institutions a competitive edge.

AI-powered algorithms have become vital tools for traders and investors. These algorithms analyze market trends, historical data, and other factors to identify patterns and make investment recommendations. By leveraging AI, financial professionals can make more informed decisions and optimize their portfolios.

Furthermore, AI plays a crucial role in risk management. Traditional risk models often fall short in assessing complex and evolving risks, making it challenging to mitigate them effectively. AI, with its machine learning capabilities, can enhance risk assessment by analyzing a wide range of variables and identifying potential threats. This helps financial institutions proactively manage risks and minimize losses.

Another area where AI has made significant strides in finance is fraud detection. With the increasing sophistication of fraudulent activities, traditional rule-based systems struggle to keep up. AI, on the other hand, can detect anomalies and unusual patterns by leveraging machine learning algorithms that constantly learn and adapt. This enables faster and more accurate detection of fraudulent transactions, protecting both financial institutions and their customers.

In conclusion, AI has had a profound impact on the finance industry and has revolutionized various aspects of it. The ability to process large amounts of data, make informed decisions, and detect risks and frauds more effectively has made AI an invaluable tool. As technology continues to advance, we can expect AI to play an even greater role in shaping the future of finance.

The Role of Artificial Intelligence in Customer Service

Artificial intelligence has had a profound impact on various industries, and one area where its influence is increasingly being felt is customer service. AI technology is transforming how businesses interact with their customers, providing enhanced communication and support.

One of the main benefits of AI in customer service is its ability to provide instant and personalized responses to customer inquiries. Through the use of chatbots and virtual assistants, businesses can now offer round-the-clock support, ensuring that customers receive the assistance they need, no matter the time of day.

Furthermore, AI-powered customer service can analyze vast amounts of data to gain insights into customer preferences and behavior. This information can then be used to tailor interactions and improve customer experiences. By understanding customer needs better, businesses can provide more relevant and targeted solutions, leading to increased customer satisfaction and loyalty.

Another crucial role of AI in customer service is its ability to automate repetitive tasks and processes. AI-powered systems can handle routine tasks such as order tracking, appointment scheduling, and basic troubleshooting, freeing up human agents to focus on more complex issues. This results in increased efficiency and productivity, as well as faster response times.

However, it’s important to note that AI should not replace human interaction entirely. While AI can handle routine tasks effectively, there are situations where human empathy and judgment are essential. Building a balance between AI and human involvement is crucial to ensure the best possible customer service experience.

In conclusion, artificial intelligence is revolutionizing customer service by providing instant and personalized support, analyzing customer data for improved experiences, and automating repetitive tasks. While AI offers numerous benefits, it is vital to strike a balance between AI and human interaction to deliver exceptional customer service in the digital age.

The Role of Artificial Intelligence in Gaming

Gaming has been greatly impacted by the advancements in artificial intelligence (AI). AI has revolutionized the way games are created, played, and experienced by both developers and players.

One of the key roles that AI plays in gaming is in creating realistic and challenging virtual opponents. AI algorithms can be programmed to assess player actions and adjust the difficulty level accordingly. This allows for a more immersive and engaging gaming experience, as players can compete against opponents that adapt to their skills and strategies.

Moreover, AI is also used in game design to create intelligent non-player characters (NPCs) that can interact with players in a more natural and realistic manner. These NPCs can simulate human-like behavior and responses, making the game world feel more alive and dynamic.

Another important role of AI in gaming is in improving game mechanics and gameplay. AI algorithms can analyze player data and preferences to provide personalized recommendations and suggestions. This helps players discover new games, unlock achievements, and improve their overall gaming experience.

Furthermore, AI has also been used in game testing and bug detection. AI algorithms can simulate various scenarios and interactions to identify potential glitches and bugs. This improves the overall quality and stability of games before their release.

In conclusion, artificial intelligence has had a profound impact on the gaming industry. It has enhanced the realism, challenge, and overall experience of games. The role of AI in gaming is ever-evolving, and it will continue to shape the future of the gaming industry.

The Future of Artificial Intelligence

Artificial intelligence (AI) has already made a significant impact on society, and its role is only expected to grow in the future. As advancements in technology continue to push boundaries, the potential applications of AI are expanding, potentially transforming various industries and aspects of our daily lives.

One of the most prominent areas where AI is expected to make a difference is in autonomous vehicles. Self-driving cars have already become a reality, and AI is set to play a crucial role in improving their capabilities further. With AI-powered sensors and algorithms, autonomous vehicles can navigate complex road conditions, reduce traffic congestion, and even enhance road safety.

Another domain that is likely to benefit from AI is healthcare. Intelligent machines can analyze vast amounts of medical data and assist doctors in making accurate diagnoses. This can lead to faster identification of diseases, more effective treatment plans, and ultimately, better patient outcomes. AI can also aid in the development of new drugs and therapies by analyzing genetic information and identifying potential targets for treatment.

In addition to healthcare and transportation, AI has the potential to revolutionize sectors such as finance, manufacturing, and agriculture. AI algorithms can analyze market data, identify trends, and make accurate predictions, enabling financial institutions to make informed investment decisions. In manufacturing, AI-powered robots can perform repetitive tasks with precision and efficiency, improving productivity and reducing costs. AI can also optimize crop production by analyzing variables such as weather conditions, soil quality, and crop health, leading to increased yields and more sustainable farming practices.

However, with the increasing integration of AI into various aspects of society, ethical considerations become crucial. As AI becomes more advanced and autonomous, questions arise about the implications of AI decision-making processes and potential biases. It is important to ensure that AI systems are designed and regulated in a way that prioritizes fairness, transparency, and accountability.

In conclusion, the future of artificial intelligence holds immense potential for transforming society in numerous ways. From autonomous vehicles and healthcare to finance and agriculture, AI is poised to revolutionize various sectors and improve our lives. However, it is essential to address ethical concerns and ensure responsible development and deployment of AI technology to maximize its positive impact on society.

The Potential Risks of Artificial Intelligence

As the impact of artificial intelligence on society continues to grow, it is important to consider the potential risks associated with this rapidly advancing technology. While intelligence can be a powerful tool for improving society, artificial intelligence poses unique challenges and dangers that must be addressed.

Unemployment and Job Displacement

One of the major concerns surrounding artificial intelligence is the potential for widespread unemployment and job displacement. As AI technology advances, machines and algorithms are becoming increasingly capable of performing tasks that were previously done by humans. This could lead to significant job losses across various industries, particularly those that rely heavily on manual labor or repetitive tasks.

Additionally, as AI systems become more sophisticated, there is a possibility that they could replace jobs that require higher levels of skill and expertise. This could result in a significant shift in the job market and create challenges for workers who are unable to adapt to these changes.

Ethical Concerns

Another potential risk of artificial intelligence is the ethical concerns that arise from its use. AI systems are designed to make decisions and take actions based on data and algorithms, but they may not always make ethical choices. This raises questions about the impact of AI on issues such as privacy, bias, and discrimination.

For example, AI algorithms may inadvertently discriminate against certain groups of people if the data used to train them is biased. This could lead to unfair outcomes in areas such as hiring, lending, and law enforcement. It is essential to address these ethical concerns and ensure that AI systems are developed and used in a responsible and equitable manner.

In conclusion, while artificial intelligence has the potential to greatly benefit society, it is important to carefully consider and address the potential risks associated with its use. Unemployment and job displacement, as well as ethical concerns, are significant challenges that must be navigated to ensure the responsible and equitable development of AI.

The Importance of Ethical Guidelines for Artificial Intelligence

As artificial intelligence (AI) continues to advance at an unprecedented pace, its impact on society becomes increasingly profound. AI has the potential to transform various industries, improve efficiency, and enhance our overall quality of life. However, with this power comes great responsibility. It is crucial to establish ethical guidelines to ensure that AI is developed and deployed in a responsible and beneficial manner.

Ethics in AI Development

Ethics play a vital role in the development of AI technology. It is essential for developers to consider the potential impact that their creations may have on society. This involves addressing questions of privacy, security, and bias. AI systems should be designed to respect fundamental human rights and ensure that they do not discriminate against certain groups of people. By setting ethical standards, we can prevent the misuse and abuse of AI technology.

The Impact on Society

Without ethical guidelines, artificial intelligence can have unintended consequences on society. For example, if AI algorithms are biased, they may perpetuate social inequalities or reinforce stereotypes. Additionally, AI systems that invade privacy or compromise security can erode trust in technology, hindering its adoption and acceptance by the public. Therefore, by implementing ethical guidelines, we can help safeguard against these negative societal impacts.

The Risks of AI without Ethical Guidelines

Artificial intelligence has the potential to revolutionize society, but it also carries risks. Without ethical guidelines in place, AI can be misused for nefarious purposes, such as surveillance and manipulation. It is crucial to establish clear boundaries and regulations to ensure that AI is used for the benefit of humanity and not to harm individuals or society as a whole.

In conclusion , the importance of ethical guidelines for artificial intelligence cannot be overstated. These guidelines serve as a compass to steer the development and deployment of AI technology in the right direction. By considering the potential impact on society and setting ethical standards, we can harness the power of AI for the betterment of humanity and create a future that is both technologically advanced and ethically responsible.

The Need for Regulation and Governance of Artificial Intelligence

The rapid development of artificial intelligence (AI) has had a profound impact on society. With the increasing deployment of intelligent systems in various domains, it is essential to establish effective regulations and governance mechanisms to ensure that AI is used responsibly and ethically.

Safeguarding Privacy and Data Security

One of the key concerns with the growing use of AI is the potential invasion of privacy and compromise of data security. Intelligent systems are capable of analyzing vast amounts of personal data, raising concerns about the misuse and unauthorized access to sensitive information. To address this, there is a need for regulations that enforce stringent data protection measures and ensure transparency in AI algorithms and data usage.

Ethical Decision-Making and Bias Mitigation

AI systems are designed to make autonomous decisions based on data and algorithms. However, the biases embedded in these systems can result in discriminatory outcomes. Regulations must be put in place to ensure that AI systems are developed and trained in a way that mitigates bias and promotes fair and ethical decision-making. This includes diverse representation in the development of AI technologies and the establishment of clear guidelines on what is considered acceptable behavior for AI systems.

Accountability and Liability

As AI systems become increasingly autonomous, it becomes crucial to determine who should be held accountable in the event of a malfunction or failure. Clear regulations need to be established to define liability in AI-related incidents and ensure that there are mechanisms in place to address any potential harm caused by AI systems. This includes the establishment of standards for testing and certification of AI systems to ensure their reliability and safety.

In conclusion, the impact of artificial intelligence on society necessitates the establishment of regulations and governance mechanisms. By addressing concerns related to privacy, bias, and accountability, we can harness the full potential of AI while ensuring that it benefits society as a whole.

The Role of Artificial Intelligence in Shaping Society’s Future

Artificial intelligence (AI) has had a profound impact on society, and its role in shaping the future cannot be understated. As technology continues to advance at an unprecedented rate, AI is becoming increasingly integrated into various aspects of our lives, from healthcare to transportation to entertainment.

One of the key impacts of AI is its ability to automate tasks that were once performed by humans, enabling us to save time and resources. For example, AI-powered chatbots have revolutionized customer service by providing prompt and efficient responses to inquiries, reducing the need for human intervention. In the healthcare industry, AI algorithms are being developed to assist doctors in diagnosing diseases and recommending treatment options, improving both accuracy and speed.

Furthermore, AI has the potential to address complex societal challenges. For instance, in the field of environmental sustainability, AI technologies can be used to optimize energy consumption, reduce waste, and develop renewable energy sources. By analyzing large amounts of data and identifying patterns, AI can help us make more informed decisions and take proactive measures to mitigate the impact of climate change.

In addition, AI has the ability to enhance our educational systems. Intelligent tutoring systems can adapt to individual learning styles and provide personalized instruction, improving student engagement and performance. AI-powered language translation tools have also facilitated global communication, breaking down language barriers and fostering cross-cultural understanding.

However, it is important to recognize that AI is not without its challenges. There are concerns regarding privacy and security, as AI relies heavily on data collection and analysis. Ethical considerations must also be taken into account, as AI systems can perpetuate biases and discrimination if not properly designed and monitored.

In conclusion, artificial intelligence plays a significant role in shaping society’s future. Its impact can be seen in various fields, from automation to sustainability to education. While there are challenges that need to be addressed, AI has the potential to revolutionize our lives and create a more efficient and equitable society.

Questions and answers

What is the impact of artificial intelligence on society.

The impact of artificial intelligence on society is significant and far-reaching. It is transforming various sectors, including healthcare, education, finance, and transportation.

How is artificial intelligence revolutionizing healthcare?

Artificial intelligence in healthcare is revolutionizing the way diseases are diagnosed and treated. It is helping doctors in making accurate diagnoses, predicting outcomes, and assisting in surgeries.

What are the ethical concerns surrounding artificial intelligence?

There are several ethical concerns surrounding artificial intelligence, such as the potential loss of jobs, bias in algorithms, invasion of privacy, and the possibility of autonomous weapons.

How can artificial intelligence improve productivity in the workplace?

Artificial intelligence can improve productivity in the workplace by automating repetitive tasks, analyzing large amounts of data quickly and accurately, and providing personalized recommendations and insights.

What are the potential risks of artificial intelligence?

The potential risks of artificial intelligence include job displacement, widening economic inequalities, security threats, loss of human control, and the potential for AI systems to be hacked or manipulated.

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Growing public concern about the role of artificial intelligence in daily life

A growing share of Americans express concern about the role artificial intelligence (AI) is playing in daily life, according to a new Pew Research Center survey.

Pew Research Center conducted this study to understand attitudes about artificial intelligence and its uses. For this analysis, we surveyed 11,201 U.S. adults from July 31 to Aug. 6, 2023.

Everyone who took part in the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this analysis , along with responses, and its methodology .

Overall, 52% of Americans say they feel more concerned than excited about the increased use of artificial intelligence. Just 10% say they are more excited than concerned, while 36% say they feel an equal mix of these emotions.

The share of Americans who are mostly concerned about AI in daily life is up 14 percentage points since December 2022, when 38% expressed this view.

Concern about AI outweighs excitement across all major demographic groups. Still, there are some notable differences, particularly by age. About six-in-ten adults ages 65 and older (61%) are mostly concerned about the growing use of AI in daily life, while 4% are mostly excited. That gap is much smaller among those ages 18 to 29: 42% are more concerned and 17% are more excited.

Rising awareness, and concern, about AI

The rise in concern about AI has taken place alongside growing public awareness. Nine-in-ten adults have heard either a lot (33%) or a little (56%) about artificial intelligence. The share who have heard a lot about AI is up 7 points since December 2022.

Those who have heard a lot about AI are 16 points more likely now than they were in December 2022 to express greater concern than excitement about it. Among this most aware group, concern now outweighs excitement by 47% to 15%. In December, this margin was 31% to 23%.

Similarly, people who have heard a little about AI are 19 points more likely to express concern today than they were in December. A majority now express greater concern than excitement (58%) about AI’s growing role in daily life, while just 8% report the opposite feeling.

Our previous analyses have found that Americans’ concerns about AI include a desire to maintain human control over these technologies , doubts that AI will improve the way things are now, and caution over the pace of AI adoption in fields like health and medicine .

Opinions of whether AI helps or hurts in specific settings

Despite growing public concern over the use of artificial intelligence in daily life, opinions about its impact in specific areas are more mixed. There are several uses of AI where the public sees a more positive than negative impact.

For instance, 49% say AI helps more than hurts when people want to find products and services they are interested in online. Just 15% say it mostly hurts when used for this purpose, and 35% aren’t sure.

Other uses of AI where opinions tilt more positive than negative include helping companies make safe cars and trucks and helping people take care of their health.

In contrast, public views of AI’s impact on privacy are much more negative. Overall, 53% of Americans say AI is doing more to hurt than help people keep their personal information private. Only 10% say AI helps more than it hurts, and 37% aren’t sure. Our past research has found majorities of Americans express concern about online privacy generally and a lack of control over their own personal information.

Public views on AI’s impact are still developing, though. Across the eight use cases in the survey, 35% to 49% of Americans say they’re not sure what impact AI is having.

Demographic differences in views of AI’s impact

There are significant demographic differences in the perceived impact of AI in specific use cases.

Americans with higher levels of education are more likely than others to say AI is having a positive impact across most uses included in the survey. For example, 46% of college graduates say AI is doing more to help than hurt doctors in providing quality care to patients. Among adults with less education, 32% take this view.

A similar pattern exists with household income, where Americans with higher incomes tend to view AI as more helpful for completing certain tasks.

A big exception to this pattern is views of AI’s impact on privacy. About six-in-ten college graduates (59%) say that AI hurts more than it helps at keeping people’s personal information private. Half of adults with lower levels of education also hold this view.

Men also tend to view AI’s impact in specific areas more positively than women. These differences by education, income and gender are generally consistent with our previous work on artificial intelligence .

Note: Here are the questions used for this analysis , along with responses, and its methodology .

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Alec Tyson is an associate director of research at Pew Research Center

Emma Kikuchi is is a research assistant focusing on science and society research at Pew Research Center

Many Americans think generative AI programs should credit the sources they rely on

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Artificial Intelligence: Positive or Negative Innovation? Essay

Introduction, artificial intelligence, positive effects of artificial innovation, negative implications of adopting ai.

Undeniably, innovations are good for the prosperity and enhancement of human’s standards of living. With this regard, technology and innovations in general should not be seen as negative. However, there is great need for caution when deciding the innovations that are helpful and those that are harmful and dangerous. This paper seeks to show that there are two effects of innovations which can either be positive or negative.

Artificial Intelligence is one of the most controversial innovations that the world has seen as per the current developments. The other closely related innovation that has also raised similar attention is the human cloning scientific development. With the artificial development, it is believed that the research will take a century before it is completed. According to the head of the Google’s self driving car project, Sebastian Thrun, artificial intelligence is taking over the world (Lemmer & Kanal 2014). He argues that while humans will still be in charge of a few aspects of life in the near future, their control will be reduced due to the development of artificial intelligence.

Artificial Intelligence can be very beneficial to the human race in a number of ways. One of the most significant ways that AI can help humans is by the degree by which it can be used in areas such as healthcare, education, and business among other areas (Matsuda, Cohen & Koedinger 2015). The advancement of AI can greatly enhance the accuracy and effectiveness of medical services hence improving humans’ livelihoods. The recent development of a self driving vehicle shows how our lives can become easier with the help of AI.

AI on the other hand has many unfavorable effects on human livelihood. For instance, the self-driven vehicle may be a good idea and its sufficiency and ability to automatically avoid collision may reduce the rate of accidents. Nonetheless, if we consider human survival, this may not be one of the best things to do. The implications of this innovation affect aspects such as employment. Having self-driven cabs in the streets will send human drivers home and reduce the rate of employment. Automated vendor machines are the best examples to show how AI can rob humanity of its natural setting.

Due to the self-running vendor machines, many energy and snack vendors have closed shopped due to lack of market. Nobody in investing on vending stores anymore since one can easily purchase a vending machine and find a vending space. The same happened with the automated teller machines which led to the laying off of several human tellers in the banking industry. The current robotics are directed and utilized under the supervision of humans and their movements and intentions are decided by people.

Determining whether AI is a friendly innovation or it is a global mistake depends on the functions that the innovation is intended to achieve. The development of AI and its inclusion in the development of war robots and drones will give the gadgets autonomous decision making capabilities (Müller & Bostrom 2014). In this case, such machines are able to reason and decide when to attack and when not to. With this power, humanity may be losing its control over the world and things can turn out to be chaotic. Giving machines the power to reason and decide independently can be such a dangerous move especially in military development and innovations.

Lemmer, F & Kanal, L 2014, ‘Qualitative probabilistic networks for planning under uncertainty’, Uncertainty in Artificial Intelligence, vol. 2, no.1, pp. 197.

Matsuda, N, Cohen, W & Koedinger, K 2015, ‘Teaching the teacher: tutoring SimStudent leads to more effective cognitive tutor authoring’, International Journal of Artificial Intelligence in Education , vol. 25, no.1, pp.1-34.

Müller, V & Bostrom, N 2014, ‘Future progress in artificial intelligence: A poll among experts,’ AI Matters , vol. 1, no.1, pp. 9-11.

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The pros and cons of artificial intelligence.

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(Photo by Andreas SOLARO / AFP) (Photo by ANDREAS SOLARO/AFP via Getty Images)

Key takeaways

• Artificial intelligence (AI) is hitting the mainstream, though the first form of AI was invented in England, way back in 1951.
• Nowadays AI is used in a wide range of applications, from our personal assistants like Alexa and Siri, to cars, factories and healthcare.
• AI has the power to make massive improvements to our quality of life, but it’s not perfect.

Artificial intelligence, or AI, is everywhere right now. In truth, the fundamentals of AI and machine learning have been around for a long time. The first primitive form of AI was an automated checkers bot which was created by Cristopher Strachey from the University of Manchester, England, back in 1951.

It’s come a long way since then, and we’re starting to see a large number of high profile use cases for the technology being thrust into the mainstream.

Some of the hottest applications of AI include the development of autonomous vehicles, facial recognition software, virtual assistants like Amazon’s AMZN Alexa and Apple’s AAPL Siri and a huge array of industrial applications in all industries from farming to gaming to healthcare.

And of course, there’s our AI-powered investing app , Q.ai.

But with this massive increase in the use of AI in our everyday lives, and algorithms that are constantly improving, what are the pros and cons of this powerful technology? Is it a force for good, for evil or somewhere in between?

Download Q.ai today for access to AI-powered investment strategies.

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Best 5% interest savings accounts of 2024, the pros of ai.

There’s no denying there are a lot of benefits to using AI. There’s a reason it’s becoming so popular, and that’s because the technology in many ways makes our lives better and/or easier.

Fewer errors

Humans are great. Really, we’re awesome. But we’re not perfect. After a few hours in front of a computer screen, we can get a little tired, a little sloppy. It’s nothing that some lunch, a coffee and a lap around the block won’t fix, but it happens.

Even if we’re fresh at the start of the day, we might be a bit distracted by what’s going on at home. Maybe we’re going through a bad breakup, or our football team lost last night, or someone cut us off in traffic on the way into work.

Whatever the reason, it’s common and normal for human attention to move in and out.

These lapses of attention can lead to mistakes. Typing the wrong number in a mathematical equation, missing out a line of code or in the case of heavy duty workplaces like factories, bigger mistakes which can lead to injury, or even death.

24/7 Uptime

Speaking of tiredness, AI doesn’t suffer from sugar crashes or need a caffeine pick-me-up to get through the 3pm slump. As long as the power is turned on, algorithms can run 24 hours a day, 7 days a week without needing a break.

Not only can an AI program run constantly, but it also runs consistently. It will do the same tasks, to the same standard, forever.

For repetitive tasks this makes them a far better employee than a human. It leads to fewer errors, less downtime and a higher level of safety. They’re all big pros in our book.

Analyze large sets of data - fast

This is a big one for us here at Q.ai. Humans simply can’t match AI when it comes to analyzing large datasets. For a human to go through 10,000 lines of data on a spreadsheet would take days, if not weeks.

AI can do it in a matter of minutes.

A properly trained machine learning algorithm can analyze massive amounts of data in a shockingly small amount of time. We use this capability extensively in our Investment Kits, with our AI looking at a wide range of historical stock and market performance and volatility data, and comparing this to other data such as interest rates, oil prices and more.

AI can then pick up patterns in the data and offer predictions for what might happen in the future. It’s a powerful application that has huge real world implications. From an investment management standpoint, it’s a game-changer.

The Cons of AI

But it’s not all roses. Obviously there are certain downsides to using AI and machine learning to complete tasks. It doesn’t mean we shouldn’t look to use AI, but it’s important that we understand its limitations so that we can implement it in the right way.

Lacks creativity

AI bases its decisions on what has happened in the past. By definition then, it's not well suited to coming up with new or innovative ways to look at problems or situations. Now in many ways, the past is a very good guide as to what might happen in the future, but it isn’t going to be perfect.

There’s always the potential for a never-before-seen variable which sits outside the range of expected outcomes.

Because of this, AI works very well for doing the ‘grunt work’ while keeping the overall strategy decisions and ideas to the human mind.

From an investment perspective, the way we implement this is by having our financial analysts come up with an investment thesis and strategy, and then have our AI take care of the implementation of that strategy.

We still need to tell our AI which datasets to look at in order to get the desired outcome for our clients. We can’t simply say “go generate returns.” We need to provide an investment universe for the AI to look at, and then give parameters on which data points make a ‘good’ investment within the given strategy.

Reduces employment

We’re on the fence about this one, but it’s probably fair to include it because it’s a common argument against the use of AI.

Some uses of AI are unlikely to impact human jobs. For example, the image processing AI in new cars which allows for automatic braking in the event of a potential crash. That’s not replacing a job.

An AI-powered robot assembling those cars in the factory, that probably is taking the place of a human.

The important point to keep in mind is that AI in its current iteration is aiming to replace dangerous and repetitive work. That frees up human workers to do work which offers more ability for creative thinking, which is likely to be more fulfilling.

AI technology is also going to allow for the invention and many aids which will help workers be more efficient in the work that they do. All in all, we believe that AI is a positive for the human workforce in the long run, but that’s not to say there won’t be some growing pains in between.

Ethical dilemmas

AI is purely logical. It makes decisions based on preset parameters that leave little room for nuance and emotion. In many cases this is a positive, as these fixed rules are part of what allows it to analyze and predict huge amounts of data.

In turn though, it makes it very difficult to incorporate areas such as ethics and morality into the algorithm. The output of the algorithm is only as good as the parameters which its creators set, meaning there is room for potential bias within the AI itself.

Imagine, for example, the case of an autonomous vehicle, which gets into a potential road traffic accident situation, where it must choose between driving off a cliff or hitting a pedestrian. As a human driver in that situation, our instincts will take over. Those instincts will be based on our own personal background and history, with no time for conscious thought on the best course of action.

For AI, that decision will be a logical one based on what the algorithm has been programmed to do in an emergency situation. It’s easy to see how this can become a very challenging problem to address.

How to use AI for your personal wealth creation

We use AI in all of our Investment Kits, to analyze, predict and rebalance on a regular basis. A great example is our Global Trends Kit , which uses AI and machine learning to predict the risk-adjusted performance of a range of different asset classes over the coming week.

These asset classes include stocks and bonds, emerging markets, forex, oil, gold and even the volatility index (VIX).

Our algorithm makes the predictions each week and then automatically rebalances the portfolio on what it believes to be the best mix of risk and return based on a huge amount of historical data.

Investors can take the AI a step further by implementing Portfolio Protection . This uses a different machine learning algorithm to analyze the sensitivity of the portfolio to various forms of risk, such as oil risk, interest rate risk and overall market risk. It then automatically implements sophisticated hedging strategies which aim to reduce the downside risk of the portfolio.

If you believe in the power of AI and want to harness it for your financial future, Q.ai has got you covered.

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Introduction

Principles of the “high road”, the return of theory x using artificial intelligence, the pluses and minuses of ai in the workplace, managing the transition: why the “wrong” choices are made, policy responses to the ai-related and other technological challenges.

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Artificial intelligence in human resource management: a challenge for the human-centred agenda?

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Peter Cappelli

Nikolai Rogovsky

The ILO human-centred agenda puts the needs, aspirations and rights of all people at the heart of economic, social and environmental policies. At the enterprise level, this approach calls for broader employee representation and involvement that could be powerful factors for productivity growth. However, the implementation of the human-centred agenda at the workplace level may be challenged by the use of artificial intelligence (AI) in various areas of corporate human resource management (HRM). While firms are enthusiastically embracing AI and digital technology in a number of HRM areas, their understanding of how such innovations affect the workforce often lags behind or is not viewed as a priority. This paper offers guidance as to when and where the use of AI in HRM should be encouraged, and where it is likely to cause more problems than it solves.

Sustainable development is at the core of national and international discussions on development issues. At the enterprise level, the ILO defines sustainability as “operating a business so as to grow and earn profit, and recognition of the economic and social aspirations of people inside and outside the organization on whom the enterprise depends, as well as the impact on the natural environment” (ILO 2007). According to the ILO, “sustainable enterprises should innovate, adopt environmentally friendly technologies, develop skills and human resources, and enhance productivity to remain competitive in national and international markets” (ILO 2007).

The ILO Centenary Declaration for the Future of Work emphasizes “the role of sustainable enterprises as generators of employment and promoters of innovation and decent work” and, in this regard, underlines the importance of “supporting the role of the private sector as a principal source of economic growth and job creation by promoting an enabling environment for entrepreneurship and sustainable enterprises […] in order to generate decent work, productive employment and improved living standards for all”. Creating “productive workplaces” and “productive and healthy conditions” of work are critical in achieving this goal (ILO 2019a).

At both the macro- and micro-levels, the ILO promotes the “high road” approach to productivity which “seeks to enhance productivity through better working conditions and the full respect for labour rights as compared to the “low road” which consists of the exploitation of the workforce” (ILO, n.d.). The “high road” is related to the ILO’s “human-centred agenda,” which is a key part of the ILO human-centred approach to the future of work highlighted in the ILO Centenary Declaration for the Future of Work and described in-depth in the related Work for a brighter future – Global Commission on the Future of Work report. This approach puts “workers’ rights and the needs, aspirations and rights of all people at the heart of economic, social and environmental policies” (ILO 2019a) and calls for investments in people’s capabilities, institutions of work and in decent and sustainable work (ILO 2019b). It is expected that such investments would be combined with people-centred approach to business practices at the workplace level.

This paper is aimed at exploring when and how AI is used in HRM, and when its impact on firm and individual performance is positive, negative or cannot be properly accessed. We start by looking at the principles of high road approach and how these principles are related to the use of AI in HRM. Then we specifically look at the pluses of minuses of AI in the workplace focusing on such aspects of HRM as hiring and work organization. We conclude with a brief overview of some possible policy responses to the AI-related and other technological challenges.

Since the Western Electric studies that were carried out in the 1920s and 1930s (Landsberger 1958), evidence has accumulated year-by-year about the advantages of taking employee management seriously: look after employees, and they will look after the employer’s interests; empower employees to make decisions, from quality circles to lean production to agile management, and performance and quality improves.

In the 1950s and early 1960s, Douglas McGregor described the developing literature on the effectiveness of management practices as “Theory Y” and contrasted it with “Theory X” which essentially views employees as simply another factor of production like raw materials in manufacturing (McGregor 1960). Frederick Taylor and his scientific management approach were arguably the originators of a sophisticated view of Theory X, which is rooted in a simple, conservative (with a small “c”) notion that employees are mainly motivated by money, need to be told what to do by experts, and will shirk their responsibilities if not watched closely. Theory Y has the much more complex but more accurate assumption that employees have many complicated motivations and if managed correctly would do the right thing for the employer even if they are not monitored or incentivized by financial rewards and punishments. The contemporary incarnation of Theory X and Y with a few new twists is the idea of a “high road” approach for Theory Y practices and a “low road” for Theory X.

In recent decades, evidence has accumulated about the advantages of Theory Y approach of taking employee management seriously and the most fundamental element of that approach, reciprocity: if employers look after the interests of their employees, then the employees in turn will be inclined to look after the interests of their employer.

The ILO data from the Better Work and Sustaining Competitive and Responsible Enterprises (SCORE) programmes 1 provides evidence of the positive effects of such an approach, showing that “improved workplace cooperation, effective workers’ representation, quality management, clean production, human resource management and occupational safety and health, as well as supervisory skills training, particularly among female supervisors, all increase productivity”. Moreover, “better management also helps to lower accidents at work 2 and employee turnover and reduces the occurrence of unbalanced production lines (where work piles up on one line while other workers are sitting idle)”. Evidence also points to “increased productivity and profitability associated with a reduction in verbal abuse and sexual harassment.” 3

Evidence has even moved past showing reductions in turnover and improvements in individual and organizational productivity to financial performance. The strongest of these studies is arguably Edmans (2011) which finds that companies making the “best places to work” ranking have higher than anticipated share prices in future years. A different study finds a similar market-beating performance for companies that have greater managerial integrity and ethics (Guiso, Sapienza and Zingales 2015). Another global study shows that companies that have better management (including more sophisticated human resource practices) perform better on a wide range of economic dimensions (Bloom and Van Reenen 2010).

None of this is to suggest that tracking employee performance, setting standards for their work efforts, and rewarding and punishing are irrelevant. However, relying solely on those tactics is not enough.

At the same time, it is important to note that at least in the short term the “low road” approach to management can allow firms to break-even or even improve economic performance (but not social outcomes) where the initial practices are simplistic. In those countries and sectors where labour standards and laws are not always respected and workers are often not organized and represented, the “low road” approach to productivity is still common, in part because it is simpler for management and may appeal to their world view that focuses on their roles. However, the “low road” approach is seeing something of a resurgence even in the most sophisticated sectors of the world leading economies as we note below.

The use of artificial intelligence (AI) in HRM can challenge the implementation of the ILO-led human-centred agenda at the workplace level. While firms are enthusiastically embracing artificial intelligence and digital technology in a number of their HRM areas, their understanding of how such innovations affect the workforce is often not viewed as a priority or lags behind (Rogovsky and Cooke 2021).

Many enterprises in both developing and developed countries are replacing the employee empowerment approach, such as quality circles and lean production, with an “optimization” approach where experts and the algorithms associated with artificial intelligence (AI) they create take back the decision-making that empowerment had created. Optimization seems to appeal to many managers as it sounds per se to be more efficient. As a result, the evidence of employee empowerment as a productivity driver is largely ignored (Cappelli 2020).

The application of data science as well as an increase in computer power in worker-related questions have spawned a huge number of applications, indeed an entire industry of vendors, offering solutions to virtually every human resource question. It takes the decision-making out of the hands of employees and their supervisors as well, turning it over to the software and ultimately the vendors and their programmers who generate answers to human resource problems. In 2020, 28 per cent of US employers report that they were using data science tools to “replace line manager duties in assigning tasks and managing performance.” 39 per cent were planning to start doing so the following year (Mercer 2020).

The use of AI in the form of data science in workforce management is not per se a bad thing. As with AI in other contexts, it may allow us to answer questions that have not been addressed before: not every AI solution is taking decisions away from humans. For example, advice to employees about possible career paths can be generated for them by machine-learning algorithms based on what has been best in the past for other workers like them. Rigorous advice on questions like this has simply not been available before. It is also the case that decisions currently made by managers and supervisors are often so poor, driven by subjectivity and bias, which makes it easier for data science solutions to do better. In hiring, for example, it is easier for data-based algorithms to do a better job than line managers who have no relevant training and base their decisions largely on subjective opinion. More generally, the lag in productivity growth across most industrialized countries has been caused, at least in part, because not enough investment was made in solutions where “capital,” which includes software, takes over tasks from workers and perform them at less cost. Consider, for example, what it would cost for a large employer that receives thousands of job applications every year if it had to do the initial classification of applications by hand instead of by applicant tracking software.

The issue in terms of guidance is knowing when the application of these AI techniques is useful (i.e. they solve new problems and handle tasks better than humans do) and where they are counterproductive (i.e. they offer no advantage over human decisions and may actually make employment relationships worse).

Finding such a mix is a challenge that involves managerial as well as moral dimensions. At the very least, we believe that when there is a choice between options that are equal in terms of organizational outcomes, employers should choose the one that is better for employees. This principle coincides with standard utilitarian views of ethics and with economic interpretations of Pareto improvements. 4 Perhaps more importantly, it draws on the legal principle in civil law of “abuse of right”, which means that simply because one party has the legal right to do something does not create the right to do it if by doing so it damages other parties without creating benefits (Mughal, unpublished).

There are still very few studies that examine the implications of artificial intelligence for corporate HRM. Tambe, Cappelli and Yakubovich (2019) noted “a substantial gap between the promise and reality of artificial intelligence” in the area of HRM. They identified four major challenges in using artificial intelligence as part of HRM:

complexity of HR phenomena, which make it difficult to model;

limitations of small data sets;

accountability issues associated with fairness and other ethical and legal constraints when decisions are made by algorithms; and

potentially negative employee reactions to managerial decisions taken based on data-based algorithms.

In particular, from both economic and social points of view there is a growing concern over the use of artificial intelligence algorithms for hiring (Cappelli 2019) and for work organization (Cappelli 2020). These issues will be considered next.

It may be easiest to grasp the general principles behind the use of AI through some common examples. Before we look into the “optimization” policies and practices per se , let us focus on hiring which is perhaps the most basic, time-consuming, and important of the employee management questions. The evidence increasingly points to the fact that we do not handle this process well even without AI: we rely on ad hoc methods of finding recruits, mainly just hoping that the right ones come to us, and then we hope that hiring managers, typically untrained in the process who rely on off-the-cuff interviews, will somehow find the best candidates to hire. Then we do not check to see whether the ones we have hired are good or bad so we do not learn from the process. What we do know is that this process gives ample room for biases to influence decisions: my personal views on what constitutes a good cultural “fit” shape who gets hired as does how much I like candidates, which is strongly correlated with how similar they are to me.

Hiring is actually a context where the prospects for algorithms are best. The way data science ideally works starts with machine learning, where the software (the “machine” in this case) looks at the attributes of as many current and past employees as possibly to see how their attributes relate to their quality as employees. The software is agnostic as to what should matter and how it should matter: relationships could be non-linear, simultaneous, in any form. It generates a single equation to measure the attributes that are associated with a good performer, not as with prior “best practice” approaches where there is one score for say IQ, one for prior experience, one for interviews, and so forth. The machine learning algorithm looks at any potential candidate and tells you how similar they are to those in the past who were your best performing employees.

The plus of this approach is that it is objective. Unlike human assessors, it will not give higher scores to more attractive applicants or those most similar to us. Algorithms have the advantage of treating all similar observations the same way: if it is counting a college degree a certain way, it does not give extra credit to the college where the boss is an alumnus. Cowgill (2020) finds that an algorithm used to predict who should advance to short-list status did a better job than human recruiters did in part because it did not over-value credentials that had higher social status such as degrees from elite universities. 5 An algorithm will also find factors that predict that humans with our more limited experience would never find. Another plus is that once set up, using algorithms to hire is remarkably cheaper than relying on humans.

The downside that is common to human assessors is that if prior experience was shaped by bias, then the algorithm will be as well. Amazon’s hiring algorithm, for example, gave higher scores to men because in the past Amazon managers had given higher scores to male employees (Cappelli 2019). Another downside is the issue now known as “explainability”: can we explain to the candidates why they were not hired when they ask why their scores were low? It is difficult for machine learning algorithms to address those questions. Complaints from gig workers that the algorithms managing them are biased have led organizations like the UK-based Workers Info Exchange to press those gig companies to explain to their contractors why and how their algorithms made the decisions they did (Murgia 2021). It also takes very large data sets to generate machine learning algorithms, and few employers hire enough employees to build their own. They are likely as a result to rely on the algorithms produced by vendors with no guarantee or even reason to believe that the vendor’s algorithm will predict hiring success for their jobs.

A related issue is that some of the factors that have been used in generating these algorithms might give us qualms. For example, the commuting distance from one’s home to a job has been shown to be a good predictor of turnover and some aspects of performance. Where one lives, therefore, shapes the likelihood of getting a job. Social media postings are sometimes used in building hiring algorithms as well. Most employers would probably want limits placed on the kind of information on which the algorithms are based, something that is not possible when one uses algorithms produced elsewhere.

From the human-centred point of view, these practices are not only potentially discriminatory as Amazon case shows, but they also prevent decent candidates getting the jobs they deserve.

If hiring is amongst the most promising uses of AI, perhaps the most troublesome is the use of software to determine workers’ schedules. This is not a new idea, but its use has expanded considerably to a wide range of jobs. 6 42 per cent of US companies now use it (Harris and Gurchensky 2020). The goal is a sensible one, to “optimize” work scheduling process in order to minimize total amount of labor needed to cover assignments and make sure that everyone is doing roughly the same amount of work allocated across similar schedules. The reason this approach is troublesome, though, is because we have other approaches that work even better where the employees themselves work out schedules through a process of negotiations and social exchange: I’ll cover for you this weekend if you take my shift next week, for example. Scheduling algorithms cut both employees and supervisors out of the process and end up being quite rigid and unable to respond to last-minute adjustments. 7 A study of optimization approaches in scheduling discovered that it increased turnover and turnover costs while adding nothing to performance outcomes (Kesavan and Kuhnen 2017). The effort to cut costs in one category (headcount) increased them in another (turnover).

The evidence that the flexible approach works is, by the standards of rigorous research, about as good as it gets. It improves a range of outcomes for employees, such as better job attitudes (Baltes et al. 1999), as well as better accommodation of life challenges outside of work including evidence that it is worth extra salary to employees (Kelly et al. 2008). For employers, it leads to higher productivity 8 . Software, in contrast, assumes that the workers are interchangeable, it imposes schedules without any consideration as to the varying needs of individual employees, and it is not at all flexible when last-minute problems pop up. As with many of these new practices, the question is, what problem is it really solving, and is the solution worse than the original problem?

Then we have situations where existing practices that involve empowering employees have worked extremely well yet there is a push to replace them with software. Beginning in the 1970s, efforts to involve employees in solving workplace problems borrowed from Japan by North American and West European companies worked so well that they spread systematically throughout industrialized countries and beyond, starting union-based cooperative programmes on safety problems, to quality circles where workers identified the causes of quality problems, and then to lean production where workers took over some of the tasks of the industrial engineers, redesigning their own jobs to improve productivity and quality. The evidence that lean production in the form of Toyota’s operating model worked so much better than anything else, especially the efforts at GM and Volkswagen to deal with productivity and quality problems with automation, was so clear that it was impossible to ignore (MacDuffie and Pil 1997). Lean production spread from there to other industries including healthcare.

Recently, though, we have seen efforts to replace the employee involvement that was at the heart of lean production with machine learning software. The new approach is called “machine vision.” Rather than having employees figure out what is wrong with their work processes, it captures what employees are doing now with cameras. Some of the new software ends there, monitoring assembly line workers constantly to make sure that they perform the tasks exactly as designed. Another software called Robotic Process Automation takes those video images and figures out how to redesign tasks to make them more efficient. In other words, it takes over the tasks the workers used to do in lean production (Simonite 2020). Other vendors reassemble jobs to push simpler tasks down to cheaper labour, 9 the classic “deskilling” practice with the classic pushback, that the narrow, simple tasks that result are so boring that engagement, commitment, and performance ultimately decline. They are performing the same tasks that workers had done before with the difference being that now, the most and possibly only interesting part of those jobs is gone. That control is what made the boring jobs tolerable.

More generally, it is also difficult to argue that paying vendors to take over a task that employees either were already doing or could do – updating the performance of tasks through lean production - is going to be cheaper, especially because lean production is a never-ending process that has to be recalibrated whenever there are changes anywhere in the system.

A final especially illustrative example comes from earlier days in IT technology and the introduction of numerically controlled machines in machining work. Here the question was, who will perform the tasks of setting up and programming those machines, something that has to be done frequently, whenever they switch over to a new product or new specifications for it. One option was to hire engineers who were skilled programmers and have them learn the context of machining that was done in different organizations. That would mean getting rid of many of the machinists. The other was to take the machinists who had the knowledge for the latter tasks and teach them programming. It was easier to do the former, but it was far cheaper in the long run to do the latter not only by avoiding the churning costs of laying off one group of workers and hiring in another or even because machinists were paid less than engineers but because the employer then created a cadre of employees with skills unique to them: unlike the programming engineers, who could easily leave for jobs elsewhere, these machinist-programmers now had the best jobs they likely could find anywhere (Kelley 1996).

There is sometimes a view stemming from simple economic assumptions that “firms” always make the most efficient choices because if they do not, they go out of business. But most businesses do fail, and it is possible for larger companies to make the wrong decisions for some time and yet stay in business. There are also so many decisions to be made in businesses that it is inevitable that we will make the wrong ones in some area.

Employers are not rational calculating machines, they are humans with the same limitations in ability to make decisions as all of us have. In the workplace, though, there are systematic reasons why employers might choose the “low road” approach even when alternatives objectively make more sense. One reason is that high road approaches that require engaging employees and soliciting their best efforts are not easy to pursue. They require sustained efforts at communication, building trust, and so forth. Not every business leader has the inclination to pursue that path. Nor do they have the knowledge base to do so. Leaders who come from engineering backgrounds are taught optimization approaches to business problems that, when focused on worker issues, come down to minimizing the costs of using them. That approach per se is not the issue as long as we have complete and accurate measures of costs and benefits 10 . But few if any employers have those measures.

Consider, for example, the cost of turnover, which is one of the most basic facts necessary to operate efficiently. Organizations that are focused on making money need to know what those costs are in order to determine how much investment is efficient to head them off. We also need to know where those costs occur. It is common if they are measured at all to simply count the administrative costs of hiring a replacement. A very careful look at these costs found that even in front-line retail jobs, two-thirds of the costs of turnover come between the time when the employee gives their notice to leave and before they actually depart. That happens in part because of negative effects on peers who remain, in part because of the demands on them of recruiting, hiring, and onboarding replacements. Those costs are massively greater than the administrative costs (Kuhn and Yu 2021). What most employers do instead is use a rough measure of the administrative costs of hiring a new worker as a proxy, which vastly undercounts the true costs. Why employers had not calculated them is in part because it is difficult to do but ultimately because of the unspoken assumption that, unlike say the costs of missing inventory, they are not big enough to bother.

At the same time, employers’ incorrect assumptions can also be explained by a lack of understanding about how humans actually behave. Many employers are simply convinced that in order to be productive the employees must be tightly controlled and refuse to accept the notion that the employees can contribute more when they are given freedom to express their views, contribute to the decision making process and are expected to take initiative 11 .  Another reason, which is investor driven, is the quirkiness of financial accounting: Chief Financial Officers (CFOs) are more likely to invest in software but not in employees because software is an asset that can be depreciated – paid off over time – whereas training and other investments in employees are current expenses that must be paid off completely in the year they are “purchased” (Cappelli 2023). 

To summarize, we offer some practical suggestions on the use of AI in corporate HRM (see Box 1). The choices as to whether to use AI tools or rely on employees depend in part – but only in part - on the nature of the tasks in question. The traditional view that we should automate the simplest tasks is not necessarily the right advice as we saw earlier with lean production where simple tasks were bundled together into jobs that workers largely controlled. There they were able to take over supervisory tasks and proved more adaptable (e.g., they did not need to be reprogrammed) than robots. Beyond the nature of the tasks, the context also determines the choice of using AI or humans.

Box 1. AI and HRM: Q&A

Governments and social partners can come up with a number of policies and practices that help guide corporate HR functions to respond to the AI-related opportunities as well as other technological challenges. Many of them are in line with the ILO-driven human-centred agenda, in particular with its pillars related to “harnessing and managing technology for decent work”, and “universal entitlement to lifelong learning that enables people to acquire skills and to reskill and upskill” 12 (ILO 2019b).

Many governments have been active in promoting a knowledge economy, the development of high-tech firms and technological upgrading in the manufacturing sector through smart manufacturing underpinned by innovations (Cooke, forthcoming). For example, in 2015, the Chinese government launched “Made in China 2025”, which is one of the national strategic initiatives aimed at transitioning China from a “large manufacturing country” to a “strong manufacturing country” through innovations related to digital technology and artificial intelligence (Kania 2019). The success of such a strategic initiative largely depends on the development of a well-educated workforce equipped with the skills and knowledge required by employers. In this case, the industrial policy of making more use of AI went together with upgrading the education and skills of workers.

Technological challenges may imply that workers will experience more transitions – as some jobs get automated. They will need more than ever support to go through a growing number of labour market transitions throughout their lives. In particular, younger workers will need help in “navigating increasingly difficult school-to-work transition” (Cooke, forthcoming). Older workers will need to be able to stay economically active as long as they want. 13 Lifelong learning policies will definitely help to be prepared for these transitions. Interestingly, data science algorithms may actually be useful here first in creating a more efficient labour market for matching workers and jobs and second by making better predictions as to what kind of skills individuals will need next based on their current experiences and jobs.

In this paper we identified some of the key challenges for high-road approach to employee management that are associated with rapid technological development and, in particular, with the use of AI. While the use of AI in HRM, in particular for hiring and work organization, is promising, still low-road approach is rather common and many suboptimal decisions are being made. The situation can be improved by broader employee engagement in HR-related decision-making process, training of managers on the principles and examples of high-road approach, as well as smart government policies. Particular attention should be paid to the development of “knowledge economy”, harnessing and managing technology for decent work, and universal entitlement to lifelong learning that enables people to acquire skills and to reskill and upskill.

As far as research is concerned, we call for more research to be done on:

pluses and minuses of using the AI in HRM;

the “natural boundaries” between the humans and AI;

how to ensure that the AI does not inherit mistakes made by the humans in the past (for example when it comes to hiring);

how AI products can become truly self-learning;

the ways to encourage fruitful collaboration of data scientists and HRM professionals in the development of the AI products; and

the role of policy makers in encouraging the use of “people-friendly” AI and in promoting high-road corporate practices.

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Peter Cappelli is the George W. Taylor Professor of Management at the Wharton School and Director of Wharton’s Center for Human Resources, University of Pennsylvania

Nikolai Rogovsky is a Senior Economist, Research Department, International Labour Office

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ISBN: 9789220394045

https://doi.org/10.54394/OHVV4382

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Pareto improvement occurs when a change in allocation does not harm anyone and helps at least one agent, given an initial allocation of goods for a set of agents.

For a very detailed discussion of how machine learning treats hiring tasks as opposed to the more traditional approach from psychology, see Liem et al. (2018).

For a review of this literature, see Van den Bergh et al. (2013).

Bernstein, Kesavan and Staats (2014) note that it is possible to try to balance the recommendations of the algorithms, but for most employers, the reason for using them is to eliminate the time needed for that process.

See, e.g., Lee and DeVoe (2012).

The software is WorkVue. See WTW (n.d.).

This includes intangible costs (such as workers’ views on firm’s reputation as an employer, job quality or equity in decision-making, etc.) that might not be fully addressed or calculated.

As noted earlier these are the two conflicting views of Theory X and Theory Y by Douglas McGregor in his seminal book The Human Side of Enterprise (1960).

Ghosheh, Lee and McCann (2006) provide an overview of the factors that need to be considered for older workers to effectively and constructively continue to contribute to the labour market.