Information Ethics

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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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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In Defense of Obfuscation

Written by Mette Leonard Høeg

At the What’s the Point of Moral Philosophy congress held at the University of Oxford this summer, there was near-consensus among the gathered philosophers that clarity in moral philosophy and practical ethics is per definition good and obscurity necessarily bad. Michael J.  Zimmerman explicitly praised clarity and accessibility in philosophical writings and criticised the lack of those qualities in especially continental philosophy, using some of Sartre’s more recalcitrant writing as a cautionary example (although also conceding that a similar lack of coherence can occasionally be found in analytical philosophy too). This seemed to be broadly and whole-heartedly supported by the rest of the participants.

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Track Thyself? Personal Information Technology and the Ethics of Self-knowledge

Written by Muriel Leuenberger

The ancient Greek injunction “Know Thyself” inscribed at the temple of Delphi represents just one among many instances where we are encouraged to pursue self-knowledge. Socrates argued that “examining myself and others is the greatest good” and according to Kant moral self-cognition is ‘‘the First Command of all Duties to Oneself’’. Moreover, the pursuit of self-knowledge and how it helps us to become wiser, better, and happier is such a common theme in popular culture that you can find numerous lists online of the 10, 15, or 39 best movies and books on self-knowledge.

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Peter Railton’s Uehiro Lectures 2022

Written by Maximilian Kiener

Professor Peter Railton, from the University of Michigan, delivered the 2022 Uehiro Lectures in Practical Ethics. In a series of three consecutive presentations entitled ‘Ethics and Artificial Intelligence’ Railton focused on what has become one the major areas in contemporary philosophy: the challenge of how to understand, interact with, and regulate AI.

Railton’s primary concern is not the ‘superintelligence’ that could vastly outperform humans and, as some have suggested, threaten human existence as a whole. Rather, Railton focuses on what we are already confronted with today, namely partially intelligent systems that increasingly execute a variety of tasks, from powering autonomous cars to assisting medical diagnostics, algorithmic decision-making, and more. Continue reading

Three Observations about Justifying AI

Written by:  Anantharaman Muralidharan, G Owen Schaefer, Julian Savulescu
Cross-posted with the Journal of Medical Ethics blog

Consider the following kind of medical AI. It consists of 2 parts. The first part consists of a core deep machine learning algorithm. These blackbox algorithms may be more accurate than human judgment or interpretable algorithms, but are notoriously opaque in terms of telling us on what basis the decision was made. The second part consists of an algorithm that generates a post-hoc medical justification for the core algorithm. Algorithms like this are already available for visual classification. When the primary algorithm identifies a given bird as a Western Grebe, the secondary algorithm provides a justification for this decision: “because the bird has a long white neck, pointy yellow beak and red eyes”. The justification goes beyond just a description of the provided image or a definition of the bird in question, and is able to provide a justification that links the information provided in the image to the features that distinguish the bird. The justification is also sufficiently fine grained as to account for why the bird in the picture is not a similar bird like the Laysan Albatross. It is not hard to imagine that such an algorithm would soon be available for medical decisions if not already so. Let us call this type of AI “justifying AI” to distinguish it from algorithms which try, to some degree or other, to wear their inner workings on their sleeves.

Possibly, it might turn out that the medical justification given by the justifying AI sounds like pure nonsense. Rich Caruana et al present a  case whereby asthmatics were deemed less at risk of dying by pneumonia. As a result, it prescribed less aggressive treatments for asthmatics who contracted pneumonia. The key mistake the primary algorithm made was that it failed to account for the fact that asthmatics who contracted pneumonia had better outcomes only because they tended to receive more aggressive treatment in the first place. Even though the algorithm was more accurate on average, it was systematically mistaken about one subgroup. When incidents like these occur, one option here is to disregard the primary AI’s recommendation. The rationale here is that we could hope to do better than by relying on the blackbox alone by intervening in cases where the blackbox gives an implausible recommendation/prediction. The aim of having justifying AI is to make it easier to identify when the primary AI is misfiring. After all, we can expect trained physicians to recognise a good medical justification when they see one and likewise recognise bad justifications. The thought here is that the secondary algorithm generating a bad justification is good evidence that the primary AI has misfired.

The worry here is that our existing medical knowledge is notoriously incomplete in places. It is to be expected that there will be cases where the optimal decision vis a vis patient welfare does not have a plausible medical justification at least based on our current medical knowledge. For instance, Lithium is used as a mood stabilizer but the reason why this works is poorly understood. This means that ignoring the blackbox whenever a plausible justification in terms of our current medical knowledge is unavailable will tend to lead to less optimal decisions. Below are three observations that we might make about this type of justifying AI.

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Ambient Intelligence

Written by Stephen Rainey

An excitingly futuristic world of seamless interaction with computers! A cybernetic environment that delivers what I want, when I want it! Or: A world of built on vampiric databases, fed on myopic accounts of movements and preferences, loosely related to persons. Each is a possibility given ubiquitous ambient intelligence. Continue reading

Ethics of the GameStop Short Squeeze

By Doug McConnell

Recently a large, loosely coordinated group of individual ‘retail investors’ have been buying up stocks that certain hedge funds had bet against (i.e. ‘shorted’). In doing so, the retail investors have driven up the price of those stocks. This has caused hedge funds that shorted the stock to lose billions of dollars and enabled a number of retail investors to get rich in the process. The phenomenon is anthropologically interesting because it is symbolic of a shift in power away from the traditional Wall Street players towards less wealthy, less well-connected individuals. But what are the ethics of this? Did Average Joe Trader just bring a measure of justice to Wall Street? Or did the mob unethically manipulate the market? If they did, are their actions any more unethical than the usual behaviour of institutional investors? Continue reading

The Doctor-Knows-Best NHS Foundation Trust: a Business Proposal for the Health Secretary

By Charles Foster

Informed consent, in practice, is a bad joke. It’s a notion created by lawyers, and like many such notions it bears little relationship to the concerns that real humans have when they’re left to themselves, but it creates many artificial, lucrative, and expensive concerns.

Of course there are a few clinical situations where it is important that the patient reflects deeply and independently on the risks and benefits of the possible options, and there are a few people (I hope never to meet them: they would be icily un-Falstaffian) whose sole ethical lodestone is their own neatly and indelibly drafted life-plan. But those situations and those people are fortunately rare. Continue reading

Regulating The Untapped Trove Of Brain Data

Written by Stephen Rainey and Christoph Bublitz

Increasing use of brain data, either from research contexts, medical device use, or in the growing consumer brain-tech sector raises privacy concerns. Some already call for international regulation, especially as consumer neurotech is about to enter the market more widely. In this post, we wish to look at the regulation of brain data under the GDPR and suggest a modified understanding to provide better protection of such data.

In medicine, the use of brain-reading devices is increasing, e.g. Brain-Computer-Interfaces that afford communication, control of neural or motor prostheses. But there is also a range of non-medical applications devices in development, for applications from gaming to the workplace.

Currently marketed ones, e.g. by Emotiv, Neurosky, are not yet widespread, which might be owing to a lack of apps or issues with ease of use, or perhaps just a lack of perceived need. However, various tech companies have announced their entrance to the field, and have invested significant sums. Kernel, a three year old multi-million dollar company based in Los Angeles, wants to ‘hack the human brain’. More recently, they are joined by Facebook, who want to develop a means of controlling devices directly with data derived from the brain (to be developed by their not-at-all-sinister sounding ‘Building 8’ group). Meanwhile, Elon Musk’s ‘Neuralink’ is a venture which aims to ‘merge the brain with AI’ by means of a ‘wizard hat for the brain’. Whatever that means, it’s likely to be based in recording and stimulating the brain.

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