AI

Are We Heading Towards a Post-Responsibility Era? Artificial Intelligence and the Future of Morality

By Maximilian Kiener. First published on the Public Ethics Blog

AI, Today and Tomorrow

77% of our electronic devices already use artificial intelligence (AI). By 2025, the global market of AI is estimated to grow to 60 billion US dollars. By 2030, AI may even boost global GDP by 15.7 trillion US dollars.  And, at some point thereafter, AI may come to be the last human invention, provided it optimises itself and takes over research and innovation, leading to what some have termed an ‘intelligence explosion’. In the grand scheme of things, as Google CEO Sundar Pichai thinks, AI will then have a greater impact on humanity than electricity and fire did.

Some of these latter statements will remain controversial. Yet, it is also clear that AI increasingly outperforms humans in many areas that no machine has ever entered before, including driving cars, diagnosing illnesses, selecting job applicants, and more. Moreover, AI also promises great advantages, such as making transportation safer, optimising health care, and assisting scientific breakthroughs, to mention only a few.

There is, however, a lingering concern. Even the best AI is not perfect, and when things go wrong, e.g. when an autonomous car hits a pedestrian, when Amazon’s Alexa manipulates a child, or when an algorithm discriminates against certain ethnic groups, we may face a ‘responsibility gap’, a situation in which no one is responsible for the harm caused by AI.  Responsibility gaps may arise because current AI systems themselves cannot be morally responsible for what they do, and the humans involved may no longer satisfy key conditions of moral responsibility, such as the following three.

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Guest Post: Dear Robots, We Are Sorry

Written by Stephen Milford, PhD

Institute for Biomedical Ethics, Basel University

 

The rise of AI presents humanity with an interesting prospect: a companion species. Ever since our last hominid cousins went extinct from the island of Flores almost 12,000 years ago, homo Sapiens have been alone in the world.[i] AI, true AI, offers us the unique opportunity to regain what was lost to us. Ultimately, this is what has captured our imagination and drives our research forward. Make no mistake, our intentions with AI are clear: artificial general intelligence (AGI). A being that is like us, a personal being (whatever person may mean).

If any of us are in any doubt about this, consider Turing’s famous test. The aim is not to see how intelligent the AI can be, how many calculations it performs, or how it shifts through data. An AI will pass the test if it is judged by a person to be indistinguishable from another person. Whether this is artificial or real is academic, the result is the same; human persons will experience the reality of another person for the first time in 12 000 years, and we are closer now than ever before. Continue reading

Reflective Equilibrium in a Turbulent Lake: AI Generated Art and The Future of Artists

Stable diffusion image, prompt: "Reflective equilibrium in a turbulent lake. Painting by Greg Rutkowski" by Anders Sandberg – Future of Humanity Institute, University of Oxford

Is there a future for humans in art? Over the last few weeks the question has been loudly debated online, as machine learning did a surprise charge into making pictures. One image won a state art fair. But artists complain that the AI art is actually a rehash of their art, a form of automated plagiarism that threatens their livelihood.

How do we ethically navigate the turbulent waters of human and machine creativity, business demands, and rapid technological change? Is it even possible?

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Cross Post: Tech firms are making computer chips with human cells – is it ethical?

Written by Julian Savulescu, Chris Gyngell, Tsutomu Sawai
Cross-posted with The Conversation

Shutterstock

Julian Savulescu, University of Oxford; Christopher Gyngell, The University of Melbourne, and Tsutomu Sawai, Hiroshima University

The year is 2030 and we are at the world’s largest tech conference, CES in Las Vegas. A crowd is gathered to watch a big tech company unveil its new smartphone. The CEO comes to the stage and announces the Nyooro, containing the most powerful processor ever seen in a phone. The Nyooro can perform an astonishing quintillion operations per second, which is a thousand times faster than smartphone models in 2020. It is also ten times more energy-efficient with a battery that lasts for ten days.

A journalist asks: “What technological advance allowed such huge performance gains?” The chief executive replies: “We created a new biological chip using lab-grown human neurons. These biological chips are better than silicon chips because they can change their internal structure, adapting to a user’s usage pattern and leading to huge gains in efficiency.”

Another journalist asks: “Aren’t there ethical concerns about computers that use human brain matter?”

Although the name and scenario are fictional, this is a question we have to confront now. In December 2021, Melbourne-based Cortical Labs grew groups of neurons (brain cells) that were incorporated into a computer chip. The resulting hybrid chip works because both brains and neurons share a common language: electricity.

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The ABC of Responsible AI

Written by Maximilian Kiener

 

Amazon’s Alexa recently told a ten-year-old girl to touch a live plug with a penny, encouraging the girl to do what could potentially lead to severe burns or even the loss of an entire limb.[1] Fortunately, the girl’s mother heard Alexa’s suggestion, intervened, and made sure her daughter stayed safe.

But what if the girl had been hurt? Who would have been responsible: Amazon for creating Alexa, the parents for not watching their daughter, or the licensing authorities for allowing Alexa to enter the market?

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A Sad Victory

I recently watched the documentary AlphaGo, directed by Greg Kohs. The film tells the story of the refinement of AlphaGo—a computer Go program built by DeepMind—and tracks the match between AlphaGo and 18-time world champion in Go Lee Sedol.

Go is an ancient Chinese board game. It was considered one of the four essential arts of aristocratic Chinese scholars. The goal is to end the game having captured more territory than your opponent. What makes Go a particularly interesting game for AI to master is, first, its complexity. Compared to chess, Go has a larger board, and many more alternatives to consider per move. The number of possible moves in a given position is about 20 in chess; in Go, it’s about 200. The number of possible configurations of the board is more than the number of atoms in the universe. Second, Go is a game in which intuition is believed to play a big role. When professionals get asked why they played a particular move, they will often respond something to the effect that ‘it felt right’. It is this intuitive quality why Go is sometimes considered an art, and Go players artists. For a computer program to beat human Go players, then, it would have to mimic human intuition (or, more precisely, mimic the results of human intuition).

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Cross Post: Biased Algorithms: Here’s a More Radical Approach to Creating Fairness

Written by Dr Tom Douglas

File 20190116 163283 1s61b5v.jpg?ixlib=rb 1.1

Our lives are increasingly affected by algorithms. People may be denied loans, jobs, insurance policies, or even parole on the basis of risk scores that they produce.

Yet algorithms are notoriously prone to biases. For example, algorithms used to assess the risk of criminal recidivism often have higher error rates in minority ethic groups. As ProPublica found, the COMPAS algorithm – widely used to predict re-offending in the US criminal justice system – had a higher false positive rate in black than in white people; black people were more likely to be wrongly predicted to re-offend.

Corrupt code.
Vintage Tone/Shutterstock

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Should PREDICTED Smokers Get Transplants?

By Tom Douglas

Jack has smoked a packet a day since he was 22. Now, at 52, he needs a heart and lung transplant.

Should he be refused a transplant to allow a non-smoker with a similar medical need to receive one? More generally: does his history of smoking reduce his claim to scarce medical resources?

If it does, then what should we say about Jill, who has never touched a cigarette, but is predicted to become a smoker in the future? Perhaps Jill is 20 years old and from an ethnic group with very high rates of smoking uptake in their 20s. Or perhaps a machine-learning tool has analysed her past facebook posts and google searches and identified her as a ‘high risk’ for taking up smoking—she has an appetite for risk, an unusual susceptibility to peer pressure, and a large number of smokers among her friends. Should Jill’s predicted smoking count against her, were she to need a transplant? Intuitively, it shouldn’t. But why not?

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Cross Post: Common Sense for A.I. Is a Great Idea. But it’s Harder Than it Sounds.

Written by Carissa Veliz

Crosspost from Slate.  Click here to read the full article

At the moment, artificial intelligence may have perfect memories and be better at arithmetic than us, but they are clueless. It takes a few seconds of interaction with any digital assistant to realize one is not in the presence of a very bright interlocutor. Among some of the unexpected items users have found in their shopping lists after talking to (or near) Amazon’s Alexa are 150,000 bottles of shampoo, sled dogs, “hunk of poo,” and a girlfriend.

The mere exasperation of talking to a digital assistant can be enough to miss human companionship, feel nostalgia of all things analog and dumb, and foreswear any future attempts at communicating with mindless pieces of metal inexplicably labelled “smart.” (Not to mention all the privacy issues.) A.I. not understanding what a shopping list is, and the kinds of items that are appropriate to such lists, is evidence of a much broader problem: They lack common sense.

The Allen Institute for Artificial Intelligence, or AI2, created by Microsoft co-founder Paul Allen, has announced it is embarking on a new research $125 million initiative to try to change that. “To make real progress in A.I., we have to overcome the big challenges in the area of common sense,” Allen told the New York Times. AI2 takes common sense to include the “infinite set of facts, heuristics, observations … that we bring to the table when we address a problem, but the computer doesn’t.” Researchers will use a combination of crowdsourcing, machine learning, and machine vision to create a huge “repository of knowledge” that will bring about common sense. Of paramount importance among its uses is to get A.I. to “understand what’s harmful to people.”

This article was originally published on Slate.  To read the full article and to join in the conversation please follow this link.

Video Series: Is AI Racist? Can We Trust it? Interview with Prof. Colin Gavaghan

Should self-driving cars be programmed in a way that always protects ‘the driver’? Who is responsible if an AI makes a mistake? Will AI used in policing be less racially biased than police officers? Should a human being always take the final decision? Will we become too reliant on AIs and lose important skills? Many interesting questions answered in this video interview with Dr Katrien Devolder.

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