Stephen Cave

AI systems have historically been used as tools that execute narrowly defined tasks. Yet recent advances in AI have unlocked possibilities for a new class of models that genuinely collaborate with humans in complex reasoning, from conceptualizing problems to brainstorming solutions. Such AI thought partners enable novel forms of collaboration and extended cognition, yet they also pose major risks—including and beyond risks of typical AI tools and agents. In this Commentary, we systematically characterize risks of AI thought partners through a novel framework that identifies risks at multiple levels of analysis, including Real-time, Individual, and Societal risks arising from collaborative cognition (RISc). We leverage this framework to propose concrete metrics for risk evaluation and finally suggest specific mitigation strategies for developers and policymakers. As AI thought partners continue to proliferate, these strategies can help prevent major harms and ensure that humans actively benefit from productive thought partnerships.

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.

It is well established both that women are underrepresented in the field of artificial intelligence (AI) and that media representations of professions have impact on career choices and prospects. We therefore hypothesised that women are underrepresented in portrayals of AI researchers in influential films. We tested this by analysing a corpus of the 142 most influential films featuring AI from 1920 to 2020, of which 86 showed one or more AI researchers, totalling 116 individuals. We found that nine AI professionals in film were women (8%). We further found that none of the 142 AI films was solely directed by a woman. We discuss a number of explanations for the paucity of women AI scientists in the media, including parallels between film and real-life gender inequality, the construction of the AI scientist as male through gendered narrative tropes, and the lack of female directors.

This commentary is a response to ‘More than Skin Deep’ by Shelley M. Park (Park, More than skin deep: A response to “The Whiteness of AI”, Philosophy & Technology, 2021), 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 (Science, Technology, & Human Values 45:538–560, 2020), 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.

Taking a principled approach is crucial to the successful use of AI in pandemic management, say Stephen Cave and colleagues

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.