Geoffrey Hinton is a pioneer in artificial intelligence with significant contributions to its development.

He is known as the 'father of artificial intelligence' and made major contributions to neural networks and deep learning.

Hinton's work on backpropagation has supported the development of most neural networks.

He has developed neural networks for image object recognition and language prediction, laying the foundation for modern language models.

Hinton has expressed concerns that AI may lead to human extinction and advocates for stronger regulation.

He believes AI could surpass human intelligence and pose significant risks to humanity.

Hinton's warnings about AI's potential dangers highlight the need for careful management and regulation.

Up to 40% of jobs worldwide could be affected by AI, sparking concerns about its impact on job markets.

Experts recommend investing in digital infrastructure, skills, and social safety nets to adapt to the changing job landscape.

The IMF warns that AI could worsen inequalities without proactive measures and supportive policies.

Policymakers must prioritize reskilling and equity to ensure humans and machines coexist harmoniously.

AI adoption could boost labor productivity, potentially increasing global GDP by 7% annually.

The public should understand that AI development requires careful consideration of its risks and benefits to mitigate its negative impact on employment and society.

The Biden administration has proposed a 'Blueprint for an AI Bill of Rights' to address AI harms.

Developing AI systems requires a multidisciplinary approach involving technical experts, social scientists, ethicists, and community representatives.

Explainability techniques are essential for fair, transparent, and accountable AI systems.

AI systems can perpetuate social biases if not carefully designed.

Regulation is evolving worldwide to address issues with AI-driven decisions.

Global AI spending is expected to rise from $150 billion in 2023 to $300 billion by 2026.

Geoffrey Hinton is a pioneer in artificial intelligence with significant contributions to its development.

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Content: Yoshua Bengio

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By Geoffrey Hinton

Yoshua Bengio is a world leader in AI. Throughout his career, Yoshua’s research has been instrumental in shaping the world’s understanding of AI’s potential—and that same expertise has made his perspective more essential than ever. His pioneering research on deep learning laid the foundations for the current revolution in AI and earned him the 2018 Turing Award—“the Nobel Prize of Computing”—alongside myself and Yann LeCun. Last year, Yoshua received Canada’s highest award for research, the Gerhard Herzberg Canada Gold Medal. He is the computer scientist with the greatest number of citations and is ranked third among all disciplines for scientific impact.

Yoshua has been concerned for many years with the social impact of artificial intelligence, contributing to the Montreal Declaration for the Responsible Development of AI. Over the past year, worried by the potential for catastrophic outcomes from future AI, he was vocal in global conversations on AI safety. He advised the U.N. Secretary-General and the U.K. AI Safety Institute, and took part in policy discussions with governments in North America, Asia, and Europe.

Hinton is a cognitive psychologist and computer scientist

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What breakthroughs has Hinton achieved through his 40+ years of work on artificial neural networks?

Breakthroughs that mimic biological brains

He is known as the 'father of artificial intelligence' and made major contributions to neural networks and deep learning.

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The White House ordered AI to be evaluated, and the European Union proposed legislation to regulate AI, teach AI to be human or the best solution!
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When I was a teenager, I studied Geographic Information System, but unfortunately I didn ’ t learn anything and I graduated with luck.
After adulthood, the accidental hit into the media circle, first and then in the print, electronic, online media to roll, slightly small.
After middle age, he completed a master ’ s degree in information technology and gradually expanded his horizons. He was determined to join a start-up company and explore different fields of technology.
In recent years, he has been actively engaged in data analysis and digital strategies, and has also been involved in the development of Location Intelligence projects. Sometimes, he can also take time to teach, not only to share what he has learned with others, but also to motivate himself to keep learning for life.
Tales of Wisdom City
Generative AI is definitely the hottest topic in the technology world today. After the popularity of ChatGPT, many companies rushed to launch similar products and gradually applied them to various industries.
But more and more people are worried that when AI is fully popularized, it will aggravate the problem of fake information flow, online fraud and other problems; even Sheffield Hinton, who is known as the "godfather of AI", is worried that AI will become too smart, afraid that it will evolve into a killer robot, which may eventually lead to the death of mankind!
How big can the negative impact of AI technology be?
How do governments around the world respond to the potential threat of AI?
Neural network is the foundation technology of ChatGPT
Fifty-one years ago, Geoffrey Hinton, who had not yet been called the “godfather of AI, ” entered the University of Edinburgh in Scotland as a graduate student and began research on artificial neural networks.
The so-called “artificial neural network ” is a mathematical model that mimics the structure and function of biological neural networks, especially the brain, and is able to learn skills by analyzing data.
This is the foundational technology behind OpenAI ChatGPT and Google Bard.
After Hinton resigned from Google, he did not have to worry about the harm his words would bring to Google, so he was interviewed by different media and said his heart: "AI may lead to the death of mankind." (Photo: University of Toronto official website)
In 2012, Hinton and his students, Ilya Sutskever and Alex Krishevsky, created a neural network system that analyzes thousands of photos and learns to recognize common things like flowers, dogs, cars, etc.
Subsequently, Hinton and two students founded DNNresearch, a startup based on this research project.
In 2013, Google acquired DNNresearch for US $44 million (approximately HK $343 million), and Hinton joined Google as Vice President and Engineering Researcher, responsible for some of the research work on Google Brain.
His protégé, Suzkville, left Google to co-found OpenAI with like-minded people, and became chief scientist, leading the research team to successfully develop the GPT model and ChatGPT.
Hinton ’ s top student, Suzkville, gave up his lucrative job at Google in 2015 to co-found OpenAI with Altman and others, and led the development of GPT and DALL-E models. (Image: University of Toronto website)
I regret working my whole life because I am worried about the dangers of AI.
In 2018, Hinton, along with University of Montreal professor Yoshua Bengio and Meta chief AI scientist Yann LeCun, won the A.M. Turing Award in recognition of their great contributions to neural networks and deep learning.
As a result, the three later became known as the “godfathers of AI ” or“ godfathers of deep learning. ”
Around the same time, Google and OpenAI began to build large-scale deep learning systems based on neural networks, which became the basis for future development of Bard and ChatGPT.
From this, it seems that Sinton plays an important role in creating the current glory of generative AI.
However, in the current era of AI, Sinton suddenly left Google in April 2023, shocking the entire AI industry.
Hinton explained that he left Google to talk freely about the potential risks of AI without harming Google.
Speaking about 50 years of research into neural networks, he said, “I ’ m very sorry about my life ’ s work. ”
The Internet will be filled with more and more fake AI-generated text, photos, and even videos, but the average person will not be able to tell the truth, Hinton said.
Moreover, sooner or later, AI technology will completely disrupt the job market, causing more and more jobs to disappear.
What is even more worrying is that at present, users can not only instruct ChatGPT to generate code, but also ask the AI to execute the code; in this way, the AI killer robots that often appear in science fiction movies will one day become a reality.
Is it not impossible for AI to destroy humanity?
Of course, this pessimistic future scenario is only a hypothetical one, but Hinton stressed that what could lead to it is already happening: a global race for AI technology has begun between Microsoft and Google, and even between China and the United States.
Most people think that the future of AI dominating everything is still far away, but he believes that AI may not take 30 to 50 years to control humans, and even find a way to kill humans, which may eventually lead to human extinction.
In the 1980s, Hinton was a professor of computer science at Carnegie Mellon University in the United States, but because he was not satisfied that the university's AI research was sponsored by the United States Department of Defense, he decided to leave the United States to teach at the University of Toronto's School of Computer Science.
He pointed out that without some kind of global regulatory agreement or regulatory mechanism, the AI arms race will intensify.
For this reason, Hinton believes that the threat posed by AI to humans may be more urgent than climate change, because the solution to climate change is obvious, as long as carbon emissions are reduced or carbon neutrality is achieved, but the current AI crisis does not know how to start. Although AI may pose an existential threat to humans, he does not agree to suspend AI research, because it can never erase the benefits and application value of AI.
Hinton believes it would be better to step up regulation of AI and make everyone aware of its potential harms.
OpenAI co-founder and CEO Sam Altman also acknowledged that ChatGPT has developed some reasoning capabilities that they can ’ t explain, and that it ’ s not entirely impossible for AI to destroy humans.
However, he said that he has installed a kill switch for ChatGPT, and if the development of ChatGPT exceeds human expectations, he will immediately press the switch to stop working.
OpenAI CEO Altman, known as the “father of ChatGPT, ” recently said he was“ a little scared ” about the potential of his children, and even admitted that there is a real possibility that AI will destroy humanity. (Image: Wikipedia)
Altman admits that ChatGPT ’ s influence in elections and the spread of disinformation is a concern, so the 2024 U.S. presidential election will be a focus for OpenAI.
(Image source: Pexels Gallery website)
The Curtain of Ignorance Makes AI Right Values
However, before we press the switch, we can try to instill the right values in AI, such as helping those most in need, not destroying humans. Google ’ s DeepMind paper published in April 2023 in the Proceedings of the National Academy of Sciences suggests that AI can be taught from the perspective of political philosopher John Rawls.
Rawls has proposed a concept called “The Veil of Ignorance ”, which aims to achieve the most equitable distribution of benefits when group decisions are made.
According to his book, The Debate, if a group of people stand behind a curtain, all of them will instantly lose their memory, become ignorant, and forget their identity and status in the real world.
The political philosopher Ross assumes that people are behind the “curtain of ignorance ” (left) and have no idea of their skills, identities, and status.
In this scenario, people tend to choose the most equitable distribution of rights for all. (Image: Wikipedia)
At this time, these people are asked to vote for a working principle for the society.
Because they do not know where they are in this society, they cannot make decisions in an egoistic way, and they can only judge according to the principle of fairness to all people.
The situation is like, at the birthday party, you are responsible for cutting the cake, because you do not know which piece you will get, so you will try to cut each piece to the same size.
Under the curtain of ignorance, fairness and justice that has nothing to do with personal interests will emerge.
For this reason, DeepMind believes that applying the “curtain of ignorance ” to AI training may help promote fairness in the process of“ aligning ” AI with human values.
By “alignment ”, it means aligning AI ’ s wishes with those of humanity.
If AI shares fair values with humans, AI will not have the intention to destroy humanity.
White House convenes tech giants to discuss AI threats
However, even if AI does not intend to destroy humans, it may still have many negative effects on human society.
The Center for Artificial Intelligence and Digital Policy (CAIDP), a U.S. technology ethics organization, has complained to the Federal Trade Commission (FTC) that OpenAI ’ s latest large language model, GPT-4, is biased and deceptive and poses a risk to privacy and public safety.
The CAIDP lists potential threats that may arise from GPT-4, including: helping hackers churn out malicious code to exacerbate cyberattacks; and biased AI training data that may reinforce social stereotypes and make people of all races and genders vulnerable to unfair treatment. The FTC is investigating OpenAI, said FTC Chairman Lina Khan. "They cannot be separated from existing legislation and the Federal Trade Commission will actively enforce them," he said.
In an effort to make GPT-4 more secure, OpenAI hired 50 experts and academics to test GPT-4 against lies, language manipulation, and dangerous scientific common sense.
The OpenAI headquarters is pictured here. (Image: Wikipedia)
In early May 2023, the White House invited Google CEO Sundar Pichai, Microsoft CEO Satya Nadella, OpenAI founder Altman, and artificial intelligence startup Anthropic founder Dario Amodei to attend a conference to discuss the impact of AI on society.
Government officials at the meeting included Vice President Kamala Harris, Biden Chief of Staff Jeff Zients, National Security Adviser Jake Sullivan, National Economic Council Chairman Lael Brainard, and Commerce Secretary Gina Raimondo.
U.S. President Joe Biden also attended the meeting.
EU to pass world ’ s first AI regulatory law
Generative AI, while helping to improve lives, can also pose security, privacy and fraud issues, says Ms Ho.
She noted that AI companies have an ethical and legal responsibility to ensure the safety of their AI products, while at the same time working with governments to protect civil rights and privacy.
She stressed that the White House will work to advance potential new legislation and support new legislation to mitigate the potential harms of AI technology.
In contrast, Europe has taken a step ahead in AI legislation.
On 11 May 2023, the European Parliament ’ s Committee on Civil Liberties and Consumer Protection overwhelmingly approved a position paper calling for restrictions on the use of AI in Europe, a crucial first step towards the adoption of the world ’ s first AI regulation bill.
The draft bill will be submitted to the full European Parliament for a vote in June of the same year, after which representatives of the European Parliament, the Council of the European Union and the European Commission will work out the final terms of the bill in tripartite talks.
The European Parliament has adopted a position paper that strengthens restrictions on biometrics, facial recognition, and the use of AI by police in routine duties.
The parliament believes the move could lead to the world ’ s first AI regulation bill. (Image: Wikipedia)
On the other hand, the White House announced that Google, Microsoft, OpenAI, NVIDIA, Stability AI, Anthropic, and Hugging Face have all committed to participating in DEF CON 31, a computer security conference to be held in August 2023, so that their AI models will be tested and evaluated by hackers and AI experts.
Experts will try to identify errors, failures, and vulnerabilities in AI tools so that AI companies can solve existing problems in their models early on.
However, these AI companies simply agree to a one-time test, rather than a commitment to a continuous evaluation when launching or improving their products, and may therefore not be sufficient to meet the regulatory requirements.
Despite this, DEF CON 31 is still seen as the first step for an AI model to be publicly evaluated.
We hope that after each important first step in legislative regulation and public evaluation, we can put generative AI trains on the right track, so that everyone can safely benefit from technological innovation.
DEF CON is one of the largest computer security conferences in the world. In addition to cybersecurity experts and hackers, participants included staff from the FBI, the Department of Defense, and the Postal Inspection Service.
The conference covered areas such as software security, computer architecture, hardware modifications, and other vulnerable areas of information. (Image: DEF CON official website)
The content of the signed and / or unsigned articles published by Economic Times is the author's personal opinion and does not represent the position of Economic Times, and the role of Economic Times is to provide a free speech platform.
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Content: Geoffrey Hinton, the godfather of AI, said after leaving Google that artificial intelligence is becoming smarter than humans.
2023 / 5 / 3 16: 46: 16 Source: IT House Author: Yuanyang Editor: Yuanyang
IT home May 3 news, one of the pioneers in the field of artificial intelligence, known as the "godfather of AI" Geoffrey Hinton (Geoffrey Hinton) recently announced that he has left the Google company he worked for ten years, because he is increasingly worried about the development of artificial intelligence technology.
In an interview with CNN's Jake Tapper today, he said that he believes artificial intelligence is becoming smarter than humans, and he wants to "blow the whistle" to remind people that they should seriously consider how to prevent artificial intelligence from controlling humans.
I'm just a scientist who suddenly realizes that these things are becoming smarter than we are, "Hinton said." I want to 'blow the whistle' in a way that says we should seriously worry about how to stop these things from taking control of us. "
Hinton was a pioneer in neural network technology, and his research laid the foundation for the artificial intelligence systems used in many products today.
IT House previously reported that he first revealed his departure in an interview with The New York Times on Monday and expressed his concerns about the potential harms of AI technology.
He said he was concerned that AI would lead to mass unemployment and create a world where many people “can ’ t know what ’ s real. ”
He also noted that the pace of advances in AI technology has far exceeded his and others ’ expectations.
If it becomes much smarter than us, it ’ s going to be very good at manipulating because it ’ s learned from us, and there are very few examples of a smarter thing being controlled by a dumber thing, ” Hinton told Tapper on Tuesday.
It will figure out ways to manipulate people into doing what it wants.
Hinton isn ’ t the only tech leader to express concerns about AI, and in March, a group of community members published a letter calling on AI labs to suspend training for at least six months on the most powerful AI systems, citing “profound risks to society and humanity. ”
Apple co-founder Steve Wozniak, one of those who signed the letter, reiterated concerns about artificial intelligence spreading false information on “CNN Morning ” on Tuesday.“ Deception will become easier for those who want to cheat you. ” Wozniak told CNN, “We ’ re not making any changes in that — we ’ re just assuming that our existing laws will fix the problem. ” Wozniak also said there may be a need for“ some type ” of regulation.
Hinton said he did not sign the petition. “I don ’ t think we can stop this progress, ” he said.“ I didn ’ t sign the petition saying we should stop studying artificial intelligence, because if the Americans stop doing research, the Chinese won ’ t. ” But he admitted that he didn ’ t have a clear solution, “I don ’ t know if we can solve this problem. ”
I believe we should try to think about ways to solve the problem.
I don't have a solution at the moment. '
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Hinton's work on backpropagation has supported the development of most neural networks.

Title:

Content: Geoffrey Hinton: GPT-4 is 100 times more knowledgeable than any human
Author: MIAO Yongqi
Published: Updated:
Computer scientist Geoffrey Hinton, known as the "godfather of artificial intelligence", announced Monday that he has resigned from Google. He has devoted his life to developing artificial intelligence systems, saying he "regrets his life's work" and is wary of the potential dangers of AI development.
In another recent interview, Hinton further talked about why he is afraid of AI development.
Hinton, 75, left Google a few days after the news broke in an interview with MIT Technology Review senior editor Will Douglas Heaven.
Hinton points out that the new generation of AI language models, especially the GPT-4, which came out in March, made him realize that these machines were moving in a direction that was much smarter than he thought, and he was afraid of these developments.
He is best known for a technique he proposed in the 1980s (with two colleagues) called backpropagation.
In short, the algorithms that allow machines to learn support almost all of today ’ s artificial neural networks, from computer vision systems to large language models.
It wasn ’ t until the 2010s that Hinton and his two recent graduates developed a “neural network ” that demonstrated its power, stronger than any computer ’ s ability to identify objects in images, and trained it to predict the next letter of a sentence, a precursor to today ’ s large-scale language patterns.
One of the students was Ilya Sutskever, the founder of OpenAI, who led the development of ChatGPT.
“We first thought it might be great. ”“ But it ’ s going to take a long time to realize that it needs to be done on a large scale to get good results. ”
In the 1980s, the discussion of “neural networks ” was considered a joke.
But Hinton doesn ’ t think so, and is working on this, software that mimics the abstract human brain and uses code as a connection between neurons. For 40 years, Hinton has viewed artificial neural networks as a clumsy attempt to mimic biological neural networks.
Now he thinks things have changed: he thinks there are better results when trying to mimic the behavior of the biological brain.
“It ’ s scary when you see it with your own eyes, ” he said.“ It ’ s a sudden change. ”
So why did ChatGPT surprise Hinton, too? He explained that the human brain has about 100 trillion connections, and “large language models have up to 500 billion, up to a trillion. ”
However, the knowledge of GPT-4 is a hundred times greater than that of any human.
So maybe it actually has a better learning algorithm than we do. "
He also pointed out that apart from the ability to learn, the ability to communicate is another kind of point.
“If you or I learn a new thing and want to teach it to another person, we can ’ t just send them a copy. ”
But artificial neural networks can do it.
It ’ s like there are 10,000 people here, and when one person learns a new thing, the other 9,999 people all learn it at the same time.
Hinton now believes that there are two types of intelligence: animal brains and artificial neural networks.
“It ’ s a completely different kind of intelligence.
“A new, better intelligence. ” Hinton believes that whether superintelligence is good or bad depends largely on whether you ’ re an optimist or a pessimist, and he says,“ mildly depressed, ” “that ’ s why I ’ m afraid. ”
“Whether these things are going to be smarter than us, I suddenly changed my mind. ”
I think they are very close to us now and they will be much smarter than us in the future, ” he said.
“How do we survive? ” he is particularly worried that people will use the tools he has developed to change the trajectory of human life, including elections and wars.
He said that he hopes to continue talking about AI security after leaving office and spend more time on “more philosophical work ”.“ As long as I ’ m still employed at Google, I can ’ t say what I want to say, ” he said. “I have a lot of good things to say about Google, and it would be more convincing if I wasn ’ t at Google. ”
Who is the godfather of AI computer scientist Geoffrey Hinton?
Hinton pioneered the basic technology of AI systems and became interested in artificial neural networks when he was a graduate student at the University of Edinburgh in 1972. In 2018, he and two experts in the field of deep learning were awarded the Turing Award, known as the “Nobel Prize in Computing ”.
What are the main concerns of “AI Godfather ” Geoffrey Hinton about the development of artificial intelligence?
He believes that the most direct impact is to promote the dissemination of false information and the loss of employment opportunities, and also worries about the use of this tool for evil, such as influencing elections and using it in war.

Which 1986 paper by Hinton became foundational for modern neural networks?

He has developed neural networks for image object recognition and language prediction, laying the foundation for modern language models.

Hinton has expressed concerns that AI may lead to human extinction and advocates for stronger regulation.

What did Hinton, the renowned British-Canadian expert reveal about AI that caused widespread concern?

Exposed serious AI risks, shocking the tech world and affecting major company stocks.

What future risk did Hinton warn about AI?

What risks did the global summit report highlight about AI?

Creating malicious software for cyberattacks, potentially posing an existential threat if humans lose control.

He believes AI could surpass human intelligence and pose significant risks to humanity.

What did Hinton, the renowned British-Canadian expert reveal about AI that caused widespread concern?

Exposed serious AI risks, shocking the tech world and affecting major company stocks.

What did Sam Altman admit about ChatGPT and the potential risks of AI?

Advanced AI could pose an existential risk to humanity.

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

Becomes uncontrollable
Poses ethical risks

What future risk did Hinton warn about AI?

What risks did the global summit report highlight about AI?

Creating malicious software for cyberattacks, potentially posing an existential threat if humans lose control.

Hinton's warnings about AI's potential dangers highlight the need for careful management and regulation.

What did Hinton, the renowned British-Canadian expert reveal about AI that caused widespread concern?

Exposed serious AI risks, shocking the tech world and affecting major company stocks.

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

Becomes uncontrollable
Poses ethical risks

What risks did the global summit report highlight about AI?

Creating malicious software for cyberattacks, potentially posing an existential threat if humans lose control.

Up to 40% of jobs worldwide could be affected by AI, sparking concerns about its impact on job markets.

Title:

Content: 09: 15, 16 January 2024
1 min.
The IMF called AI the cause of future problems with jobs for people
This is already a bell.
The International Monetary Fund (IMF) warns that artificial intelligence will affect nearly 40% of jobs worldwide.
According to the IMF analysis, developed economies are most at risk, while emerging markets and low-income countries will face fewer problems.
Kristalina Georgieva, managing director of the IMF, noted in her blog that in most scenarios, AI is likely to exacerbate overall inequality.
This is an alarming trend that politicians need to actively address in order to prevent further intensification of social tensions.
The IMF statement highlights the importance of taking measures to mitigate the potential negative effects of automation and AI in the labor market.
It is important that governments and organizations develop strategies to support workers and ensure they are retrained and adapted to the changing landscape of jobs.
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What risks did Hinton associate with AI's rapid advancement?

Experts recommend investing in digital infrastructure, skills, and social safety nets to adapt to the changing job landscape.

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IMF chief warns AI will hit labor markets like a tsunami, affecting 40% of global jobs
2024 / 5 / 15 16: 50: 48 Source: IT Home Author: Tracing wave (internship)
According to Reuters, International Monetary Fund (IMF) Managing Director Kristalina Georgieva recently said at an event in Zurich, Switzerland, that AI will cause a “tsunami ” blow to the labor market.
Georgieva said: “We have little time to prepare people and businesses for this. ”
As previously reported by IT House, she believes AI could affect 40% of global jobs in the next two years, while the AI wave will affect more jobs in advanced economies, reaching 60%.
Georgieva added: "If we manage it properly, it (AI) can greatly increase productivity, but it can also lead to more misinformation and increase social inequality."
Speaking ahead of the spring meetings of the World Bank and the International Monetary Fund in April, the IMF chief said how to deal with the emergence of AI has become the biggest problem facing the global economy.
At the time, she said, “AI could dramatically accelerate the fourth industrial revolution. ”
This brings huge potential benefits, but also introduces risks...
“Investments in digital infrastructure, skills, and strong social security networks will determine the speed of AI adoption and its impact on productivity. ”
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The IMF warns that AI could worsen inequalities without proactive measures and supportive policies.

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Content: AI Study Claims 2 Out Of 5 Human Jobs Will Be Disrupted Or Lost To Artificial Smarts

Artificial intelligence features and tools are making their way into every facet of life, from your computer at home to search engines and productivity tools. With this advancement, there has been a growing concern that AI will be replacing jobs, and research indicates that 40% of all jobs globally could be at risk of negative impact. While this seems like a startling figure, it opens the discussion of what we humans will do next.

Earlier this week, the International Monetary Fund (IMF), a Washington, DC-based financial institution with ties to the United Nations and IMF 190-member countries supporting economic stability, published research on “Artificial Intelligence and the Future of Work.” This research reported that “40 percent of global employment is exposed to AI, with advanced economies at greater risk,” the latter closing in on 60 percent of jobs exposed due to a prevalence of cognitive-task-oriented jobs. However, with this higher risk, more advanced economies also have the means to better leverage the benefits of AI than a developing or emerging economy.

The research also notes that AI will not be limited to any one job role or sector and that, effectively, every job is in the line of fire, as it were. Regardless of job roles, it is further noted that “the advent of AI could exacerbate inequality within countries along various dimensions, such as the income level of individuals, their education level, or their gender.” Effectively, AI has the potential to replace just about any job, increasing socioeconomic divides in nations that see broad adoption of the technology.

Moreover, the report finds that AI will affect older individuals more than younger individuals and that prior education influences individuals’ adaptability. However, these negative downsides also raise the opportunity for positive change. Namely, people should be looking for the implementation of policies that ensure social cohesion and “promote the equitable and ethical integration of AI and train the next generation of workers in these new technologies” while also “protect[ing] and help[ing] retrain workers currently at risk from disruptions.”

In short, given the looming economic and social pressures, individuals should look for policies that support and protect them in a time of technological advancement rather than shut down the advancement. This research also raises the question of when we as a society determine that it is a bad thing to have our jobs replaced by robots. It simply means that we have a new challenge in supporting each other better as humanity pivots to advancing not only the sciences, but arts and creative industries as well.
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Policymakers must prioritize reskilling and equity to ensure humans and machines coexist harmoniously.

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Content: Home » Inequality Deepens as IMF Warns Nearly 40% of Jobs Worldwide at Risk from AI

Inequality Deepens as IMF Warns Nearly 40% of Jobs Worldwide at Risk from AI

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Artificial Intelligence: A New Paradigm

The Evolving Impact of Artificial Intelligence

The rapid rise of artificial intelligence (AI) is poised to reshape industries and the global job market. According to the International Monetary Fund (IMF), nearly 40% of jobs worldwide could be affected by AI, fueling concerns about deepening inequality.

A Call for Proactive Measures

In a recent blog post, IMF chief Kristalina Georgieva urged governments to establish robust social safety nets and implement retraining programs in order to mitigate the potential negative impact of AI on employment. Georgieva emphasized that policymakers must address this issue proactively to prevent further exacerbation of social tensions.

Complex Consequences and Varied Impacts

As AI becomes increasingly integrated into various sectors, it will both complement and disrupt human labor. Advanced economies are expected to face more significant challenges than emerging markets due in part to the higher vulnerability of white-collar workers compared to manual laborers.

“In most scenarios, AI will likely worsen overall inequality, a troubling trend that policymakers must proactively address…”
– Kristalina Georgieva, IMF Chief

Detailed analysis by the IMF indicates that as much as 60% of jobs in developed economies could be impacted by AI. While approximately half may benefit from increased productivity facilitated by AI implementation, others may experience reduced demand for their skills or even complete job loss.

“For the other half, AI applications may execute key tasks currently performed by humans… leading to lower wages and reduced hiring.”
– Kristalina Georgieva, IMF Chief

Lower-income countries and emerging markets are also expected to encounter challenges, considering the limited infrastructure and skilled workforce required to harness the full potential of AI technologies. Consequently, there is a growing risk that AI could further amplify existing inequalities.

Read more:  Oprah Winfrey Reveals the Secret to Her Slimmed-Down Frame: Weight-Loss Medication and a Holistic Approach

Building Bridges for Inclusive AI Adoption

Acknowledging both the risks and opportunities presented by AI, Georgieva emphasizes that targeted efforts should be made to ensure widespread benefits for humanity. Governments should foster collaboration with industries to establish comprehensive strategies:

  • Investing in infrastructure and facilitating access to technology in low-income countries
  • Promoting lifelong learning initiatives to equip workers with adaptable skill sets
  • Fostering public-private partnerships aimed at leveraging AI for societal welfare
  • “AI will transform the global economy… Let’s make sure it benefits humanity.”
    – Kristalina Georgieva, IMF Chief

    Considerations for a Balanced Future

    The integration of AI into daily work routines presents a unique set of challenges that must be met head-on. While concerns over job displacement prevail, evidence suggests that widespread adoption of AI has the potential to boost labor productivity considerably.

    “While workplaces may shift, widespread adoption of AI could ultimately increase labor productivity and boost global GDP by 7% annually…”
    – Goldman Sachs economists (March 2023 estimate)

    To shape an inclusive future where both humans and machines coexist harmoniously, policymakers must envision frameworks that prioritize reskilling programs and equitable policies.

    In Conclusion: Embracing an Era of Human-Machine Collaboration

    The increasing presence of artificial intelligence brings with it unmatched opportunities for innovation and progress. As this technology continues to evolve, the global community must come together to ensure that its implementation is guided by principles of fairness and societal well-being. By embracing AI advancements within inclusive frameworks, we can build a future where humanity reaps the rewards of this transformative technological wave.

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AI adoption could boost labor productivity, potentially increasing global GDP by 7% annually.

The public should understand that AI development requires careful consideration of its risks and benefits to mitigate its negative impact on employment and society.

What did Hinton, the renowned British-Canadian expert reveal about AI that caused widespread concern?

Exposed serious AI risks, shocking the tech world and affecting major company stocks.

What does Hinton warn could happen to AI if companies and regulators fail to implement effective rules and controls?

What risks did Hinton associate with AI's rapid advancement?

The Biden administration has proposed a 'Blueprint for an AI Bill of Rights' to address AI harms.

Title:

Content: ContributorsPublishersAdvertisers

White House Cautions Schools Against ‘Continuous Surveillance’ of Students

The 74
 2 days ago

Updated, Oct. 5

The Biden administration on Tuesday urged school districts nationwide to refrain from subjecting students to “continuous surveillance” if the use of digital monitoring tools — already accused of targeting at-risk youth — are likely to trample students’ rights.

The White House recommendation was included in an in-depth but non-binding white paper, dubbed the “Blueprint for an AI Bill of Rights,” that seeks to rein in the potential harms of rapidly advancing artificial intelligence technologies, from smart speakers featuring voice assistants to campus surveillance cameras with facial recognition capabilities.

The blueprint, which was released by the White House Office of Science and Technology Policy and extends far beyond the education sector, lays out five principles: Tools that rely on artificial intelligence should be safe and effective, avoid discrimination, ensure reasonable privacy protections, be transparent about their practices and offer the ability to opt out “in favor of a human alternative.”

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Though the blueprint lacks enforcement, schools and education technology companies should expect greater federal scrutiny soon. In a fact sheet , the White House announced that the Education Department would release by early 2023 recommendations on schools’ use of artificial intelligence that “define specifications for the safety, fairness and efficacy of AI models used within education” and introduce “guardrails that build on existing education data privacy regulations.”

During a White House event Tuesday , Education Secretary Miguel Cardona said officials at the department “embrace utilizing Ed Tech to enhance learning” but recognize “the need for us to change how we do business.” The future guidance, he said, will focus on student data protections, ensuring that digital tools are free of biases and incorporate transparency so parents know how their children’s information is being used.

“This has to be baked into how we do business in education, starting with the systems that we have in our districts but also teacher preparation and teacher training as well,” he said.

Amelia Vance, president and founder of Public Interest Privacy Consulting, said the document amounts to a “massive step forward for the advocacy community, the scholars who have been working on AI and have been pressuring the government and companies to do better.”

The blueprint, which offers a harsh critique of online proctoring tools and systems that predict student success based on factors like poverty, follows in-depth reporting by The 74 on schools’ growing use of digital surveillance and the tech’s impact on student privacy and civil rights.

Related: Exclusive Data: An Inside Look at the Spy Tech That Followed Kids Home for Remote Learning — and Now Won’t Leave

But local school leaders should ultimately decide whether to use digital student monitoring tools, said Noelle Ellerson Ng, associate executive director of advocacy and governance at AASA, The School Superintendents Association. Ellerson Ng opposes “unilateral federal action to prohibit” the software.

“That’s not the appropriate role of the federal government to come and say this cannot happen,” she said. “But smart guardrails that allow for good practices, that protect students’ safety and privacy, that’s a more appropriate role.”

The nonprofit Center for Democracy and Technology praised the report. The group recently released a survey highlighting the potential harms of student activity monitoring on at-risk youth, who are already disproportionately disciplined and referred to the police as a result. In a statement Tuesday, it said the blueprint makes clear “the ways in which algorithmic systems can deepen inequality.”

“We commend the White House for considering the diverse ways in which discrimination can occur, for challenging inappropriate and irrelevant data uses and for lifting up examples of practical steps that companies and agencies can take to reduce harm,” CEO Alexandra Reeve Givens said in a media release.

The document also highlights several areas where artificial intelligence has been beneficial, including improved agricultural efficiency and algorithms that have been used to identify diseases. But the technologies, which have grown rapidly with few regulations, have introduced significant harm, it notes, including discrimination in tools that screen job applicants and facial recognition technology that can contribute to wrongful arrests .

After the pandemic shuttered schools nationwide in early 2020 and pushed students into makeshift remote learning, companies that sell digital activity monitoring software to schools saw an increase in business. But the tools have faced significant backlash for subjecting students to relentless digital surveillance.

Related: With ‘Don’t Say Gay’ Laws & Abortion Bans, Student Surveillance Raises New Risks

In April, Massachusetts Sens. Elizabeth Warren and Ed Markey warned in a report the technology could carry significant risks — particularly for students of color and LGBTQ youth — and promoted a “need for federal action to protect students’ civil rights, safety and privacy.” Such concerns have become particularly acute as states implement new anti-LGBTQ laws and abortion bans and advocates warn that digital surveillance tools could expose expose youth to legal peril.

Vance said that she and others focused on education and privacy “had no idea this was coming,” and that it would focus so heavily on schools. Over the last year, the department sought input from civil rights groups and technology companies, but Vance said that education groups had lacked a meaningful seat at the table.

The lack of engagement was apparent, she said, by the document’s failure to highlight areas where artificial intelligence has been beneficial to students and schools. For example, the document discusses a tool used by universities to predict which students were likely to drop out. It considered students’ race as a predictive factor, leading to discrimination fears. But she noted that if implemented equitably, such tools can be used to improve student outcomes.

“Of course there are a lot of privacy and equity and ethical landmines in this area,” Vance said. “But we also have schools who have done this right, who have done a great job in using some of these systems to assist humans in counseling students and helping more students graduate.”

Related: Trevor Project to Refund Donation From Student Surveillance Company Accused of LGBTQ Bias Following 74 Investigation

Ellerson Ng, of the superintendents association, said her group is still analyzing the blueprint’s on-the-ground implications, but that student data privacy efforts present schools with “a balancing act.”

“You want to absolutely secure the privacy rights of the child while understanding that the data that can be generated, or is generated, has a role to play, too, in helping us understand where kids are, what kids are doing, how a program is or isn’t working,” she said. “Sometimes that’s broader than just a pure academic indicator.”

Others have deemed the blueprint toothless and just another policy position in a crowded field of recommendations from civil rights groups and tech companies. Some of the most outspoken privacy proponents and digital surveillance critics, such as Albert Fox Cahn, founder and executive director of the Surveillance Technology Oversight Project, argued it falls short of a critical policy move: outright bans.

As Cahn and other activists mount campaigns against student surveillance tools, they’ve highlighted how student data can wind up in the hands of the police .

“When police and companies are rolling out new and destructive forms of AI every day, we need to push pause across the board on the most invasive technologies,” he said in a media release. “While the White House does take aim at some of the worst offenders, they do far too little to address the everyday threats of AI, particularly in police hands.”

Developing AI systems requires a multidisciplinary approach involving technical experts, social scientists, ethicists, and community representatives.

Title:

Content: Miranda Bogen is creating solutions to help govern AI

To give AI-focused women academics and others their well-deserved — and overdue — time in the spotlight, TechCrunch is launching a series of interviews focusing on remarkable women who’ve contributed to the AI revolution. We’ll publish several pieces throughout the year as the AI boom continues, highlighting key work that often goes unrecognized. Read more profiles here.

Miranda Bogen is the founding director of the Center of Democracy and Technology’s AI Governance Lab, where she works to help create solutions that can effectively regulate and govern AI systems. She helped guide responsible AI strategies at Meta and previously worked as a senior policy analyst at the organization Uptown, which seeks to use tech to advance equity and justice.

Briefly, how did you get your start in AI? What attracted you to the field?

I was drawn to work on machine learning and AI by seeing the way these technologies were colliding with fundamental conversations about society — values, rights, and which communities get left behind. My early work exploring the intersection of AI and civil rights reinforced for me that AI systems are far more than technical artifacts; they are systems that both shape and are shaped by their interaction with people, bureaucracies, and policies. I’ve always been adept at translating between technical and non-technical contexts, and I was energized by the opportunity to help break through the appearance of technical complexity to help communities with different kinds of expertise shape the way AI is built from the ground up.

What work are you most proud of (in the AI field)?

When I first started working in this space, many folks still needed to be convinced AI systems could result in discriminatory impact for marginalized populations, let alone that anything needed to be done about those harms. While there is still too wide a gap between the status quo and a future where biases and other harms are tackled systematically, I’m gratified that the research my collaborators and I conducted on discrimination in personalized online advertising and my work within the industry on algorithmic fairness helped lead to meaningful changes to Meta’s ad delivery system and progress toward reducing disparities in access to important economic opportunities.

How do you navigate the challenges of the male-dominated tech industry and, by extension, the male-dominated AI industry?

I’ve been lucky to work with phenomenal colleagues and teams who have been generous with both opportunities and sincere support, and we tried to bring that energy into any room we found ourselves in. In my most recent career transition, I was delighted that nearly all of my options involved working on teams or within organizations led by phenomenal women, and I hope the field continues to lift up the voices of those who haven’t traditionally been centered in technology-oriented conversations.

What advice would you give to women seeking to enter the AI field?

The same advice I give to anyone who asks: find supportive managers, advisors, and teams who energize and inspire you, who value your opinion and perspective, and who put themselves on the line to stand up for you and your work.

What are some of the most pressing issues facing AI as it evolves?

The impacts and harms AI systems are already having on people are well-known at this point, and one of the biggest pressing challenges is moving beyond describing the problem to developing robust approaches for systematically addressing those harms and incentivizing their adoption. We launched the AI Governance Lab at CDT to drive progress in both directions.

What are some issues AI users should be aware of?

For the most part, AI systems are still missing seat belts, airbags, and traffic signs, so proceed with caution before using them for consequential tasks.

What is the best way to responsibly build AI?

The best way to responsibly build AI is with humility. Consider how the success of the AI system you are working on has been defined, who that definition serves, and what context may be missing. Think about for whom the system might fail and what will happen if it does. And build systems not just with the people who will use them but with the communities who will be subject to them.

How can investors better push for responsible AI?

Investors need to create room for technology builders to move more deliberately before rushing half-baked technologies to market. Intense competitive pressure to release the newest, biggest, and shiniest new AI models is leading to concerning underinvestment in responsible practices. While uninhibited innovation sings a tempting siren song, it is a mirage that will leave everyone worse off.

AI is not magic; it’s just a mirror that is being held up to society. If we want it to reflect something different, we’ve got work to do.

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Content: UAE participates in setting global standards for fair, accountable and understandable systems
Artificial intelligence.
Scientific Maturity with Moral Dilemmas
T + T - normal size
With the increasing use of artificial intelligence technologies in vital sectors that directly affect our lives, such as health, education, mobility, agriculture and even arts and space, experts believe that it is possible to develop a super artificial intelligence that reaches the point of controlling the emotions and motivations of people and thus their reality.
Will we be the ones who shape the reality and future of artificial intelligence systems or vice versa?
The more pressing question is, can we ensure that artificial intelligence is harnessed as a moral responsibility to improve human lives?
The rapid progress of artificial intelligence in recent years has led groups of experts to call for the need to develop safeguards to protect humanity from the dangers of artificial intelligence.
Given the gravity of the issue, many government bodies, civic organizations, private companies, think tanks, and research groups are working to develop principles governing AI trends such as the Barcelona Declaration, the Montreal Declaration, the Google Principles, and the Microsoft Principles.
One such organization is the Future of Life Institute, a nonprofit research and outreach organization founded in Boston in 2014 to monitor existential threats to humanity.
The institute was founded by MIT cosmologist Max Tegmark, Skype co-founder Jan Tallinn and DeepMind research scientist Victoria Krakow.
The institute worked with AI researchers and developers as well as scientists from many disciplines to create the 23 guidelines now referred to as the Asilomar Principles, which aim for AI technologies to develop reliably, safely and comprehensively.
A recent study by the Future of Life Institute suggests that AI is unlikely to show human emotions such as love or hate, and that there is no reason to expect AI to automatically deliberate good or evil.
But experts at the institute suggest two scenarios in which AI could become an imminent threat: The first scenario is that humans program AI to do something destructive, such as “autonomous ” or autonomous weapons — AI systems programmed to kill and capable of easily causing mass casualties in the wrong person ’ s hands.
Moreover, an AI arms race could inadvertently lead to an AI war that also leads to heavy casualties.
To avoid “losing ” to the other side, these weapons can be designed to be extremely difficult to stop, and humans can easily lose control in that situation.
This risk exists even with “narrow ” — or limited — AI, but it grows with increasing levels of AI and autonomy.
The other scary scenario is that the AI is programmed to do something useful, but it may develop a destructive way to achieve its goal, which can happen when we fail to fully align the AI goals with our goals, which is very difficult.
For example, if you ask a smart, obedient car to take you to the airport as soon as possible, you may arrive “as soon as possible, ” but you will be chased by police helicopters and probably passed out at speed.
The car didn ’ t do “what it wanted, ” which was to arrive at least safely, but it did literally what you asked.
If a super-intelligent system is tasked with an ambitious geoengineering project, it could cause havoc in our ecosystem as a side effect, and human attempts to stop it are seen as a “threat ” to be met.
Benefit
His Excellency Omar bin Sultan Al Olama, Minister of State for Artificial Intelligence, Digital Economy and Teleworking Applications, said: “The UAE government attaches great importance to ensuring the optimal use of AI solutions and capabilities, and adopts within its future directions to make the most of this advanced technology, with full commitment to employ it with high ethical responsibility centered on people and community service. ”
To achieve this, the UAE government, through the UAE Artificial Intelligence and Blockchain Council, has formed a dedicated subcommittee to ensure effective governance and regulation of AI technology.
Among the initiatives related to the ethics of artificial intelligence applications worked on by the Council is the self-assessment tool for artificial intelligence applications, which was launched at the level of the Government of Dubai, and is being developed and launched at the level of the federal government.
Principles
He added: “This assessment is based on four basic principles: inclusion: It means that the benefits of artificial intelligence include all members of society, and that governance systems apply to this technology globally, while respecting the dignity, rights and privacy of individuals.
Humanity: AI should be beneficial to humanity and consistent with human values, both in the short term and in the long term.
And safety: AI systems should be safe, and be harnessed in the service of humanity.
AI systems must be fair, transparent, accountable and understandable.
In addition to these principles and standards, the UAE Council for Artificial Intelligence and Blockchain, represented by its subcommittee of artificial intelligence experts in the country, participates in the development and formulation of global standards, through the World Organization for Standards, with a group of experts. ”
He pointed out that the committee is currently working on the development of technical standards for artificial intelligence, including the standards of "explainable artificial intelligence", which is one of the most important technical topics that allow to verify the compatibility of models of artificial intelligence with the basic principles of its use.
The committee has played an important role in the development of these standards, and it is expected that they will be adopted in the country once approved by the technical committee of the World Organization for Standards, to control and ensure the optimal employment of artificial intelligence applications.
Data protection
As for the data used to train artificial intelligence models, which is one of the most important possible elements of these applications, the UAE has issued the “Personal Data Protection Law ”, which aims to protect the data of individuals available to companies from the uses that violate privacy in the training of artificial intelligence models and other uses.
The Office of Artificial Intelligence, Digital Economy and Telework Applications, in collaboration with Oxford University, is working to train the employees of ministries and federal and local government agencies through the “Artificial Intelligence Program ” to develop the ability of trainees to analyze the security and ethical risks that may result from the wrong adoption of artificial intelligence technologies.
Rules.
Ghassan Abu Ajamiya, director of strategic planning at the Emirates Alliance for Technical Solutions, said: “Science at the beginning is simple, but with the development of these sciences, including artificial intelligence, societies need laws and legislation to regulate its transactions, and what happened in our digital life is that developments are faster than legislation, and giant companies began to impose their way of dealing with data. ”
Abu Ajima pointed out that most of those companies and owners of platforms that contain millions of data are American companies, so the European Union began its journey to address these trends by issuing a few days ago the Digital Services Law, which aims to get rid of harmful or toxic content on the Internet, and under this decision will be responsible for their algorithms, disclosure in all transparency, and required to assess the risks in content and mitigate it to protect users. Prevent the rapid spread of misinformation, combat hate speech, and avoid putting unsafe products on the market.
About a month earlier, the Digital Markets Act was announced, with the aim of reducing the broad dominance of companies in the digital economy, and the beginning of the union with the laws was when the data privacy protection system was issued in 2018.
He added: “There is no doubt that the set of decisions issued by the European Union can serve as a platform to help draw up a legal framework for legislation that can be built on and adapted according to the values and ethics that suit each country or region, and this will undoubtedly contribute to improving how user data is used and reduce its exploitation by IT giants that own major social media platforms. ”
A proactive step
Mohamed Amin, senior vice president of Dell Technologies in the Middle East, said many companies today rely on AI technologies to get accurate and quick answers to difficult issues they may face.
He added: “Today, we rely more and more on artificial intelligence models to make different decisions, whether about approving bank loan applications or helping customers decide on their next holiday destination, this technology is able to calculate the smallest details and differences.
So we need to make sure that AI systems are ethical and incorruptible, and that people are able to understand how decisions are made through this technology.
There is no doubt that artificial intelligence has the ability to calculate huge amounts of data and improve decision-making processes, but we cannot lose sight of the fact that the decisions made by AI systems affect the lives of humans, they are a machine that can currently use big data to create better information than humans, but they do not understand the context, have no emotions or ethics, and are unable to think outside the box.
When there are no clear checks and balances in the functioning of these systems, they can negatively affect the diversity of our ideas and our excellence, by treating us as a homogeneous group, and providing only structured content that does not respect our differences.
Artificial intelligence has been described as the most important technology under development today.
However, we need to place it under the proper supervision of governments and regulators, who will be instrumental in helping companies deal with the ethical side and shortcomings of these systems.
Challenges
“The ethical challenges of AI are not limited to regulating the collection and circulation of personal data, but rather to putting in place controls to prevent the employment of AI to make decisions or to subject, compel or influence certain people or their decisions through the processing of their personal data, whether without their consent or in a way that may prejudice or affect their rights, ” said lawyer Mujahed Sebai.
He added: "Many legal and ethical questions related to the use of artificial intelligence are also raised about the mechanism of making decisions or correcting them if they are wrong or determining who is responsible for them, especially in the event of damage, whether material or moral, and may take biased decisions or lead to injustice or discrimination in all its forms or to monopolize the huge data that may be collected about people or institutions."
“To deal with these challenges, all stakeholders, from governments to individuals, must work together to identify the direct or long-term effects of AI, to develop strategies and systems to ensure that it is employed through a humane approach that respects the human principles and values on which our societies and the rights of individuals are based, without negatively affecting or reprogramming those values, especially by outsiders, whether for political, economic or security considerations. ”
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Explainability techniques are essential for fair, transparent, and accountable AI systems.

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Content: As Artificial Intelligence's (AI) use in business and government increases by leaps and bounds, the need for transparent, fair, and safe governance standards has moved off the planning list into action.

Introduction

As Artificial Intelligence's (AI) use in business and government increases by leaps and bounds, the need for transparent, fair, and safe governance standards has moved off the planning list into action. In a vacuum of universal guidelines, many corporations and governments have already begun to create their own standards to address the key topics of model transparency, explainability, and fairness. While these guidelines have been an excellent start, there is still a need for organizations to control and guide their AI development while also keeping the current and emerging regulatory environment in mind.

Due to the nascent nature of the field, there are few widely implemented and agreed upon best practices. The 2023 World Privacy Forum (WPF) report on Assessing and Improving AI Governance Tools1 attempts to remedy this gap by highlighting examples across 6 categories:

  1. Practical Guidance consisting of general education information on AI governance,
  2. Self-assessment Questionnaires,
  3. Procedural Framework with step-by-step workflows on AI systems and/or improvements,
  4. Technical Framework,
  5. Technical Code/Software,
  6. Scoring or Classification Output.

WPF are using the following definition of AI governance tools to shape their work: "socio-technical tools for mapping, measuring, or managing AI systems and their risks in a manner that operationalizes or implements trustworthy AI". In other words, when making decisions with AI, it is necessary to account for the system's transparency, explainability, fairness and potential societal impact.

Table 1: AI Governance tool types Lexicon

Category Tools
Practical Guidance Includes general educational information, practical guidance, or other consideration factors
Self-assessment Questions Includes assessment questions or detailed questionnaire
Procedural Framework Includes process steps or suggested workflow for AI system assessments and/or improvements
Technical Framework Includes technical methods or detailed technical process guidance or steps
Technical Code or Software Includes technical methods, including use of specific code or software
Scoring or Classification Output Includes criteria for determining a classification, or a mechanism for producing a quantifiable score or rating reflecting a particular aspect of an AI system

Table 1: AI Governance Tool Types Lexicon
Source: World Privacy Forum2

These tools mentioned are in active use worldwide, making them an excellent starting point to ensure compliance with present and future regulations. Without the appropriate tools in place, it is nearly impossible to ensure a trustworthy AI future. A survey conducted by the AI Risk and Security (AIRS) group evaluated the current status of AI governance across their members (Figure 1). The survey revealed that there is room to improve, with only 30% of enterprises have established roles or responsibilities for AI systems, and just 20% have a centrally managed and budgeted department dedicated to AI governance.

"Global spending on AI is expected to increase from $150 billion in 2023 to $300 billion by 2026. The use of AI is moving at a rapid pace with regulators' eyes keeping a close watch, and we're seeing leaders in the TMT industry create their own governance tools as a commercial and operational imperative." George Haitsch, WTW Technology, Media and Telecom Industry Leader

"

Global spending on AI is expected to increase from $150 billion in 2023 to $300 billion by 2026."

George Haitsch | WTW Technology, Media and Telecom Industry Leader

Implementing several AI governance tools fosters a collective understanding and assessment of possible risks and limitations in AI explainability, transparency, and fairness across different departments. All personnel engaging with AI models should possess awareness regarding its capabilities and shortcomings, emphasizing the importance of maintaining a skeptical perspective toward AI outputs.

Figure 1: The current status of AI governance in enterprises

Notes: Respondents included professionals from technology risk, information security, legal, privacy, architecture, model risk management, and other fields, working in financial and technology organizations, as well as academic institutions.

Emerging AI governance regulations

Regulation is evolving in different pathways around the world. Policy makers across the globe are hurriedly addressing the issues, gaps, and limitations of AI driven decisions.

This has prompted a surge of literature and research into the subject. In July 2023, the U.S. Biden administration announced3 that Amazon, Anthropic, Google, Inflection, Meta, Microsoft and OpenAI will self-regulate their AI development.

  • Congress' blueprint for an AI Bill of Rights4 provides practical guidance through a set of five principles and practices to government agencies and a call to action for technology companies, researchers, and civil society to build these protections5.

The EU produced a similar set of ethical guidelines to the US with 7 key requirements including transparency, accountability, and respect for privacy and data protection6. They also proposed a classification scoring system. In the European Union's A.I. Act, regulations are assigned proportionate to the level of risk posed by an AI tool: unacceptable, high, limited, and minimal risk.

  • Unacceptable risks that would be banned include AI systems that perform social scoring of individuals or real-time facial recognition in public places. Other tools carrying less risk, such as software that generates manipulated videos and "deepfake" images must disclose that people are seeing AI-generated content. Violators could be fined 6% of their global sales. Minimally risky systems include spam filters and AI-generated video games.

Canada produced a mandatory risk assessment tool in 2020 named as the Algorithmic Impact Assessment (AIA).7

  • It consists of 51 risk and 34 mitigation self-assessment questions to determine the impact level of an automated decision-system. The final assessment scores are based on factors such as system's design, algorithm, decision type, impact, and data.

Singapore launched a technical framework and software with AI Verify8 in partnership with companies from different sectors and scale including AWS, DBS Bank, Google, Meta, Microsoft, Singapore Airlines, and Standard Chartered Bank. It became open source in June 2023.

In June 2023, China revealed that it was developing an "Artificial Intelligence Law" that could offer practical guidance, a technical framework, and a classification scoring system. Rather than devising a comprehensive regulatory plan, China has chosen to regulate AI through individual legislation, with specific laws addressing issues such as algorithms, generative AI, and deepfakes.

Global companies will be facing increasing pressure to comply with regulatory standards in AI governance. This often presents a challenge as they must also comply with other regulatory measures, such as ESG. As a result, companies may spend more time disclosing information about their algorithms rather than making tangible progress.

Risks and limitations – Explainability, oversimplification & bias

Several AI governance tools, including fairness AI auditing software, may be limited in their application to specific phases of the AI life cycle. AI fairness tools might solely address fairness concerns during the model training stage of AI development. However, ensuring fairness at one stage does not guarantee its persistence throughout the entire AI life cycle.

AI Life Cycle
Source: NHS England "Creating an international approach to AI for healthcare"9

Addressing bias in AI models through technical code or software is just the beginning for AI governance. Companies must extend their focus beyond the technical developers' Proof Of Value. AI fairness auditing implemented across the entire AI life cycle along with comprehensive documentation is key. The documentation should incorporate various AI governance tools understandable to both technical and non-technical audiences.

Technical developers are focused on achieving explainability by describing the mechanisms of an AI system or algorithm through software/code. One of the major problems for evaluating explainability from the technical developer's point of view is oversimplification.

  • For example, the utilization of both SHAP10 (Shapley Additive exPlanations) and LIME11 (Local Interpretable Model-agnostic Explanations) for model explainability has seen an uptick. This rise in popularity is partly owed to the methods' model-agnostic nature, allowing evaluation of any model, and to their abundance of user-friendly documentation.

In a typical scenario, a data scientist may opt for SHAP or LIME to show how an output was determined for a single instance of a model input, such as a specific decision or prediction, rather than the entire model. In other words, both methods operate by approximating more intricate, non-linear models (often referred to as "black-box" models) with simpler linear models, potentially resulting in misleading outcomes.

Global companies will be facing increasing pressure to comply with regulatory standards in AI governance.

Oversimplification and the potential lack of critical context poses a significant challenge. Some algorithms designed for specific social settings may be inaccurately applied in different contexts, resulting in technical inaccuracies and misguided outcomes. The NIST AI Risk Management Framework12, examined in Part II of the report, acknowledges the shortcomings of metrics used to measure AI risk, citing the risk of oversimplification, gaming, lack of critical nuance, and unexpected reliance.

This deficiency in contextual understanding could lead to unfair resource allocation or access, with implications across various areas such as mortgage lending, employment screening, college admissions, child welfare, and medical diagnoses.

Business recommendations and model framework

An AI governance framework should encompass internal governance structures and measures, define the extent of human involvement in AI decision-making, address operations management, facilitate stakeholder interaction and communication, and illustrate de-risking strategies for AI across the entire business to mitigate large-scale failures.

1. Internal governance structures and measures

Internal governance structures and measures play a crucial role in ensuring robust oversight of an organization's use of AI. They should be addressing risks and integrating ethical considerations through ethics review boards.

Organizations may explore features for their internal governance structures, such as clear roles and responsibilities for ethically deploying AI. A decentralized governance mechanism might be considered when a centralized approach is suboptimal, bringing ethical considerations into day-to-day decision-making.

2. Determining the level of human involvement in AI decision-making

Before deploying AI solutions, organizations are advised to determine how much influence humans have in the process. The levels addressing human oversight with AI are listed below:

Human-in-the-loop: In this model, human oversight is active, with people retaining full control. The AI provides recommendations or input to the humans driving the process.

Human-out-of-the-loop: In this model, there is no human oversight, and the AI system has full control without the option of human override.

Human-over-the-loop (or human-on-the-loop): This model involves human oversight to the extent that the human is in a supervisory role, with the ability to take control in the face of unexpected events. Humans can adjust parameters during the algorithm's operation. Examples include: AI assisted medical diagnoses, product recommendations, or GPS navigation systems.

3. Data management and governance

The individuals involved in model training and selection, whether internal staff or external providers, should work collaboratively. Data accountability practices, including understanding data lineage, ensuring data quality, and minimizing inherent bias, are essential. Organizations must understand the lineage of data and address factors affecting data quality.

Minimizing inherent bias involves being aware of biases in datasets, using heterogeneous datasets, and employing different datasets for training, testing, and validation. Regular review and updating of datasets, even if non-personal, are recommended for accuracy, quality, and reliability. Good data accountability practices apply even when using non-personal data or anonymized personal data in AI model training.

In deploying AI algorithms, organizations must iterate through model development until achieving the most suitable results for their use case. The interaction between data and algorithms/models is vital. Datasets, sourced from various places, both personal and non-personal, are integral to the AI solution's success.

4. Stakeholder interaction and communication

Effective AI governance relies on clear end-to-end communication with various stakeholders such as developers, executives, regulators, external AI tool customers, internal business users, and more. Achieving this involves concise and accessible AI documentation, addressing model gaps and biases, and specifying appropriate use cases.

Transparent communication, simple user interfaces, opt-out mechanisms, and feedback channels are paramount for user understanding and interaction. Organizations should regularly assess their AI governance against evolving ethical standards and share the results with relevant stakeholders. Meta's "Why am I seeing this?"13 feature exemplifies transparency in advertising and machine learning model training. As AI progresses, AI governance evolves. WTW will continue to keep pace with that change and explore collaboration amongst employees, executives, users, and researchers on examining and enhancing AI for optimal governance.

Footnotes

1. World Privacy Forum - Risky Analysis - December 2023. Return to article undo

2. Model Artificial Intelligence Governance Framework - Second Edition. Return to article undo

3. Readout of White House Listening Session on Tech Platform Accountability. Return to article undo

4. A Framework for U.S. AI Governance: Creating a Safe and Thriving AI Sector. Return to article undo

5. Generative AI: Implications for Trust and Governance. Return to article undo

6. What is AI Verify? Return to article undo

7. Artificial intelligence act. Return to article undo

8. Creating an international approach to AI for healthcare. Return to article undo

9. AI Risk Management Framework. Return to article undo

10. An introduction to explainable AI with Shapley values. Return to article undo

11. LIME - Local Interpretable Model-Agnostic Explanations. Return to article undo

12. AI Risk Management Framework. Return to article undo

13. Increasing Our Ads Transparency. Return to article undo

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.

AI systems can perpetuate social biases if not carefully designed.

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Have you heard the unsettling stories that have people from all walks of life worried about AI?

A 24-year-old Asian MIT graduate asks AI to generate a professional headshot for her LinkedIn account. The technology lightens her skin and gives her eyes that are rounder and blue. ChatGPT writes a complimentary poem about president Biden, but refuses to do the same for former president Trump. Citizens in India take umbrage when an LLM writes jokes about primary figures of the Hindu faith, but not those associated with Christianity or Islam.

These stories fuel a feeling of existential dread by presenting a picture where AI puppet masters use technology to establish ideological dominance. We often avoid this topic in public conversations about AI, especially since the demands of professionalism ask us to separate personal concerns from our work lives. Yet, ignoring problems never solves them, it simply allows them to fester and grow. If people have a sneaking suspicion that AI is not representing them, and may be actively discriminating against them, it’s worth discussing.

What are we calling AI?

Before diving into what AI may or may not be doing, we should define what it is. Generally, AI refers to an entire toolkit of technologies including machine learning (ML), predictive analytics and large language models (LLM). Like any tool, it is important to note that each specific technology is meant for a narrow range of use cases. Not every AI tool is suited for every job. It is also worth mentioning that AI tools are relatively new and still under development. Sometimes even using the right AI tool for the job can still yield undesired results.

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For example, I recently used ChatGPT to assist with writing a Python program. My program was supposed to generate a calculation, plug it into a second section of code and send the results to a third. The AI did a good job on the first step of the program with some prompting and help as expected.

But When I proceeded to the second step, the AI inexplicably went back and modified the first step. This caused an error. When I asked ChatGPT to fix the error, it produced code that caused a different error. Ultimately, ChatGPT kept looping through a series of identical program revisions that all produced a few variations of the same errors.

No intention or understanding is happening on the part of ChatGPT here, the tool’s capabilities are simply limited. It became confused at around 100 lines of code. The AI has no meaningful short-term memory, reasoning or awareness, which might partly be related to memory allocation; but it is clearly deeper than that. It understands syntax and is good at moving large lumps of language blocks around to produce convincing results. At its core, ChatGPT doesn’t understand it is being asked to code, what an error is, or why they should be avoided, no matter how polite it was for the inconvenience of one.

I’m not excusing AI for producing results that people find offensive or disagreeable. Rather, I’m highlighting the fact that AI is limited and fallible, and requires guidance to improve. In fact, the question of who should provide AI moral guidance is really what lurks at the root of our existential fears.

Who taught AI the wrong beliefs?

Much of the heartache surrounding AI involves it producing results that contradict, dismiss or diminish our own ethical framework. By this I mean the vast number of beliefs humans adopt to interpret and evaluate our worldly experience. Our ethical framework informs our views on subjects such as rights, values and politics and are a concatenation of sometimes conflicting virtues, religion, deontology, utilitarianism, negative consequentialism and so on. It is only natural that people fear AI might adopt an ethical blueprint contradictory to theirs when not only do they not necessarily know their own — but they are afraid of others imposing an agenda on them.

For example, Chinese regulators announced China’s AI services must adhere to the “core values of socialism” and will require a license to operate. This imposes an ethical framework for AI tools in China at the national level. If your personal views are not aligned with the core values of socialism, they will not be represented or repeated by Chinese AI. Consider the possible long-term impacts of such policies, and how they may affect the retention and development of human knowledge.

Worse, using AI for other purposes or suborning AI according to another ethos is not only an error or bug, it’s arguably hacking and potentially criminal.

Dangers in unguided decisioning

What if we try to solve the problem by allowing AI to operate without guidance from any ethical framework? Assuming it can even be done, which is not a given, this idea presents a couple of problems.

First, AI ingests vast amounts of data during training. This data is human-created, and therefore riddled with human biases, which later manifest in the AI’s output. A classic example is the furor surrounding HP webcams in 2009 when users discovered the cameras had difficulties tracking people with darker skin. HP responded by claiming, “The technology we use is built on standard algorithms that measure the difference in intensity of contrast between the eyes and the upper cheek and nose.”

Perhaps so, but the embarrassing results show that the standard algorithms did not anticipate encountering people with dark skin.

A second problem is the unforeseen consequences that can arise from amoral AI making unguided decisions. AI is being adopted in multiple sectors such as self-driving cars, the legal system and the medical field. Are these areas where we want expedient and efficient solutions engineered by a coldly rational and inhuman AI? Consider the story recently told (then redacted) by a US Air Force colonel about a simulated AI drone training. He said:

“We were training it in simulation to identify and target a SAM threat. And then the operator would say ‘yes, kill that threat.’ The system started realizing that while they did identify the threat, at times the human operator would tell it not to kill that threat — but it got its points by killing that threat. So what did it do? It killed the operator. It killed the operator, because that person was keeping it from accomplishing its objective.

We trained the system — ‘Hey don’t kill the operator — that’s bad. You’re gonna lose points if you do that’. So what does it start doing? It starts destroying the communication tower that the operator uses to communicate with the drone to stop it from killing the target.”

This story caused such an uproar that the USAF later clarified the simulation never happened, and the colonel misspoke. Yet, apocryphal or not, the tale demonstrates the dangers of an AI operating with moral boundaries and the potentially unforeseen consequences.

What is the solution?

In 1914, Supreme Court Justice Louis Brandeis said: “Sunlight is said to be the best of disinfectants.” A century later, transparency remains one of the best ways to combat fears of subversive manipulation. AI tools should be created for a specific purpose and governed by a review board. This way we know what the tool does, and who oversaw its development. The review board should disclose discussions involving the ethical training of the AI, so we understand the lens through which it views the world and can review the evolution and development of the guidance of AI over time.

Ultimately, the AI tool developers will decide which ethical framework to use for training, either consciously or by default. The best way to ensure AI tools reflect your beliefs and values is to train them and inspect them yourself. Fortunately, there is still time for people to join the AI field and make a lasting impact on the industry.

Lastly, I’d point out that many of the scary things we fear AI will do already exist independent of the technology. We worry about killer autonomous AI drones, yet the ones piloted by people right now are lethally effective. AI may be able to amplify and spread misinformation, but we humans seem to be pretty good at it too. AI might excel at dividing us, but we have endured power struggles driven by clashing ideologies since the dawn of civilization. These problems are not new threats arising from AI, but challenges that have long come from within ourselves.

Most importantly, AI is a mirror we hold up to ourselves. If we don’t like what we see, it is because the accumulated knowledge and inferences we’ve given AI is not flattering. It might not be the fault of these, our latest children, and might be guidance about what we need to change in ourselves.

We could spend time and effort trying to warp the mirror into producing a more pleasing reflection, but will that really address the problem or do we need a different answer to what we find in the mirror?

Sam Curry is VP and CISO of Zscaler.

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