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.

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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 safety and governance, or increasing the risk of conflict. It explores the role of the research community in responding 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 responsible progress.

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.

In this paper, the authors clarify various philosophical issues surrounding the concept of an ethical machine and the aims of machine ethics, outlining the potential risks that must be considered and managed.

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Taking a principled approach is crucial to the successful use of AI in pandemic management, say Stephen Cave and colleagues Competing interests: We have read and understood BMJ policy on declaration of interests and have no relevant interests to declare. Provenance and peer review: Commissioned; externally peer reviewed. This collection of articles was proposed by the WHO Department of Digital Health and Innovation and commissioned by The BMJ. The BMJ retained full editorial control over external peer review, editing, and publication of these articles. Open access fees were funded by WHO.