Stephen Cave

New types of artificial intelligence (AI), from cognitive assistants to social robots, are challenging meaningful comparison with other kinds of intelligence. How can such intelligent systems be catalogued, evaluated, and contrasted, with representations and projections that offer meaningful insights? To catalyse the research in AI and the future of cognition, we present the motivation, requirements and possibilities for an atlas of intelligence: an integrated framework and collaborative open repository for collecting and exhibiting information of all kinds of intelligence, including humans, non-human animals, AI systems, hybrids and collectives thereof. After presenting this initiative, we review related efforts and present the requirements of such a framework. We survey existing visualisations and representations, and discuss which criteria of inclusion should be used to configure an atlas of intelligence.

In this book, Stephen Cave and John Martin Fischer debate whether or not we should choose to live forever. This ancient question is as topical as ever: while billions of people believe they will live forever in an otherworldly realm, billions of dollars are currently being poured into anti-ageing research in the hope that we will be able to radically extend our lives on earth. But are we wise to wish for immortality? What would it mean for each of us as individuals, for society, and for the planet? In this lively and accessible debate, the authors introduce the main arguments for and against living forever, along with some new ones. They draw on examples from myth and literature as well as new thought experiments in order to bring the arguments to life. Cave contends that the aspiring immortalist is stuck on the horns of a series of dilemmas, such as boredom and meaninglessness, or overpopulation and social injustice. Fischer argues that there is a vision of radically longer lives that is both recognizably human and desirable. This book offers both students and experienced philosophers a provocative new guide to a topic of perennial importance.

Responsible AI (RAI) guidelines aim to ensure that AI systems respect democratic values. While a step in the right direction, they currently fail to impact practice. Our work discusses reasons for this lack of impact and clusters them into five areas: (1) the abstract nature of RAI guidelines, (2) the problem of selecting and reconciling values, (3) the difficulty of operationalising RAI success metrics, (4) the fragmentation of the AI pipeline, and (5) the lack of internal advocacy and accountability. Afterwards, we introduce a number of approaches to RAI from a range of disciplines, exploring their potential as solutions to the identified challenges. We anchor these solutions in practice through concrete examples, bridging the gap between the theoretical considerations of RAI and on-the-ground processes that currently shape how AI systems are built. Our work considers the socio-technical nature of RAI limitations and the resulting necessity of producing socio-technical solutions.

This commentary is a response to ‘More than Skin Deep’ by Shelley M. Park, and a development of our own 2020 paper ‘The Whiteness of AI’. We aim to explain how representations of AI can be varied in one sense, whilst not being diverse. We argue that Whiteness’s claim to universal humanity permits a broad range of roles to White humans and White-presenting machines, whilst assigning a much narrower range of stereotypical roles to people of colour. Because the attributes of AI in the popular imagination, such as intelligence, power and passing as human, are associated by the White racial frame with Whiteness, AI is cast predominantly as White. Following Sparrow, we suggest this presents a dilemma for those creating or representing AI. We discuss three possible solutions: avoiding anthropomorphisation, explicitly critiquing racial role-typing, and representing powerful AI as non-White.

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.

Many authors have proposed constraining the behaviour of intelligent systems with ‘machine ethics’ to ensure positive social outcomes from the development of such systems. This paper critically analyses the prospects for machine ethics, identifying several inherent limitations. While machine ethics may increase the probability of ethical behaviour in some situations, it cannot guarantee it due to the nature of ethics, the computational limitations of computational agents and the complexity of the world. In addition, machine ethics, even if it were to be ‘solved’ at a technical level, would be insufficient to ensure positive social outcomes from intelligent systems.