政治自由主义 = Political liberalism

In the past five years, private companies, research institutions and public sector organizations have issued principles and guidelines for ethical artificial intelligence (AI). However, despite an apparent agreement that AI should be ‘ethical’, there is debate about both what constitutes ‘ethical AI’ and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analysed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted, why they are deemed important, what issue, domain or actors they pertain to, and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.

The global landscape of AI ethics guidelines

Conclusions and Recommendations, are particularly interesting in view of the controversies aroused by the Warnock Report. Many of the recommendations contained here are similar to Warnock's (for example, concerning the legitimacy of AID children, the need for a licensing authority to supervise the work ofAID and IVF centres, etc), but others are at odds with the corresponding Warnock recommendations. In general, the authors place higher value on the family as an institution than did the Warnock Committee and display a much livelier awareness of the possible social dangers of the new techniques. One weakness of the book is that since its authors are approaching these topics from the standpoint of social scientists, their recommendations for legislative action which surely must be based on properly ethical considerations, not merely sociological ones seem devoid of any satisfactory rational support. For example, they concede that experimentation on human embryos is an objectionable practice, since 'the material acting as the subject of the experimentation is a human being at the beginning of its individual development' (p 178); but the practical recommendation which they make concerning this practice is disappointingly feeble:

https://jme.bmj.com/content/medethics/12/3/165.full.pdf

Ethics and the Limits of Philosophy

Authored by Dr. Safiya U. Noble, an assistant professor at the University of Southern California Annenberg School of Communication, this text is one of the preeminent works that explicitly addresse...

Algorithms of Oppression: How Search Engines Reinforce Racism

The Right and the Good

The last few years have seen a proliferation of principles for AI ethics. There is substantial overlap between different sets of principles, with widespread agreement that AI should be used for the common good, should not be used to harm people or undermine their rights, and should respect widely held values such as fairness, privacy, and autonomy. While articulating and agreeing on principles is important, it is only a starting point. Drawing on comparisons with the field of bioethics, we highlight some of the limitations of principles: in particular, they are often too broad and high-level to guide ethics in practice. We suggest that an important next step for the field of AI ethics is to focus on exploring the tensions that inevitably arise as we try to implement principles in practice. By explicitly recognising these tensions we can begin to make decisions about how they should be resolved in specific cases, and develop frameworks and guidelines for AI ethics that are rigorous and practically relevant. We discuss some different specific ways that tensions arise in AI ethics, and what processes might be needed to resolve them.

https://dl.acm.org/doi/pdf/10.1145/3306618.3314289

The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions

This paper categorizes some of the fundamental hopes and fears expressed in imaginings of artificial intelligence (AI), based on a survey of 300 fictional and non-fictional works. The categories are structured into four dichotomies, each comprising a hope and a parallel fear, mediated by the notion of control. These are: the hope for much longer lives (‘immortality’) and the fear of losing one’s identity (‘inhumanity’); the hope for a life free of work (‘ease’), and the fear of becoming redundant (‘obsolescence’); the hope that AI can fulfil one’s desires (‘gratification’), alongside the fear that humans will become redundant to each other (‘alienation’); and the hope that AI offers power over others (‘dominance’), with the fear that it will turn against us (‘uprising’). This Perspective further argues that these perceptions of AI’s possibilities, which may be quite detached from the reality of the technology, can influence how it is developed, deployed and regulated. A survey of 300 fictional and non-fictional works featuring artificial intelligence reveals that imaginings of intelligent machines may be grouped in four categories, each comprising a hope and a parallel fear. These perceptions are decoupled from what is realistically possible with current technology, yet influence scientific goals, public understanding and regulation of AI.

Hopes and fears for intelligent machines in fiction and reality

How AI is perceived by the public can have significant impact on how it is developed, deployed and regulated Some commentators argue that perceptions are currently distorted or extreme This paper discusses the results of a nationally representative survey of the UK population on their perceptions of AI The survey solicited responses to eight common narratives about AI (four optimistic, four pessimistic), plus views on what AI is, how likely it is to impact in respondents' lifetimes, and whether they can influence it 42% of respondents offered a plausible definition of AI, while 25% thought it meant robots Of the narratives presented, those associated with automation were best known, followed by the idea that AI would become more powerful than humans Overall results showed that the most common visions of the impact of AI elicit significant anxiety Only two of the eight narratives elicited more excitement than concern (AI making life easier, and extending life) Respondents felt they had no control over AI's development, citing the power of corporations or government, or versions of technological determinism Negotiating the deployment of AI will require contending with these anxieties

https://dl.acm.org/doi/pdf/10.1145/3306618.3314232

"Scary Robots": Examining Public Responses to AI

The rhetoric of the race for strategic advantage is increasingly being used with regard to the development of artificial intelligence (AI), sometimes in a military context, but also more broadly. This rhetoric also reflects real shifts in strategy, as industry research groups compete for a limited pool of talented researchers, and nation states such as China announce ambitious goals for global leadership in AI. This paper assesses the potential risks of the AI race narrative and of an actual competitive race to develop AI, such as incentivising corner-cutting on safe-ty and governance, or increasing the risk of conflict. It explores the role of the research community in respond-ing to these risks. And it briefly explores alternative ways in which the rush to develop powerful AI could be framed so as instead to foster collaboration and respon-sible progress.

An AI Race for Strategic Advantage: Rhetoric and Risks

This paper focuses on the fact that AI is predominantly portrayed as white—in colour, ethnicity, or both. We first illustrate the prevalent Whiteness of real and imagined intelligent machines in four categories: humanoid robots, chatbots and virtual assistants, stock images of AI, and portrayals of AI in film and television. We then offer three interpretations of the Whiteness of AI, drawing on critical race theory, particularly the idea of the White racial frame. First, we examine the extent to which this Whiteness might simply reflect the predominantly White milieus from which these artefacts arise. Second, we argue that to imagine machines that are intelligent, professional, or powerful is to imagine White machines because the White racial frame ascribes these attributes predominantly to White people. Third, we argue that AI racialised as White allows for a full erasure of people of colour from the White utopian imaginary. Finally, we examine potential consequences of the racialisation of AI, arguing it could exacerbate bias and misdirect concern.

Stephen Cave

Papers

The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions

Hopes and fears for intelligent machines in fiction and reality

"Scary Robots": Examining Public Responses to AI

An AI Race for Strategic Advantage: Rhetoric and Risks

The Whiteness of AI