Kerry McInerney

This introduction to the special issue “Rethinking Data Colonialism: Local Negotiations with Data, Postcoloniality, and Power” examines the emergence of the term data colonialism, and its relationship to other key ideas like digital colonialism and AI colonialism. It opens by examining how the term “data colonialism” has been used primarily to designate the asymmetric, unequal, and extractive relationship of Big Tech with countries in the Global South. We argue that existing scholars have paid inadequate attention to historical specificity of colonial domination and the neocolonial and settler colonial forms of state domination in ongoing data colonialism projects. In order to address this lacuna, it is imperative to pay attention to the differences, conflicts, as well as the local forms of opposition within specific regions and countries beyond global North–South binaries. This introduction highlights the key goal of the special issue, which is to foreground how data colonialism operates with, through, and alongside local power relations, and how local actors negotiate and resist these extractive regimes. It also articulates themes––such as everyday lived experiences of data colonialism, and how people resist colonialist AI practices and the elites’ appropriation of anti-colonial rhetoric in postcolonial contexts––as addressed by the special issue articles. Finally, the Introduction, reiterating the contributions of the special issue, ponders the question of decolonizing AI and what decolonial AI futures look like beyond the epistemological and conceptual parameters of the Western academy.

The rhetoric of an ‘AI arms race’ between the United States and China has become increasingly prominent over the past 5 years, despite warnings that it is unnecessarily polarising and undermines safe and ethical artificial intelligence (AI) development. However, existing critiques of the AI arms race narrative engage only sparingly with the racialised dimensions of this discourse. In this article, I draw on the rich theoretical insights of Asian American and Asian diaspora studies to show how the AI arms race narrative is deeply racialised in two key ways. First, I show how the rhetoric of an AI arms race builds upon the myth of a ‘Clash of Civilizations’ between the West and the East. This civilisational rhetoric constitutes China and the United States as distinct and mutually opposed cultural entities, thus foreclosing the possibility of more peaceful and cooperative alternatives to the AI arms race. Second, I demonstrate how the US–China AI arms race specifically draws on previous racialised configurations of anti-Asian sentiment, such as techno-Orientalism and the Yellow Peril. I coin the term Yellow Techno-Peril to connote how older European and Americans fears of being overrun or controlled by China are reproduced in the AI arms race. I close by offering recommendations to key stakeholders such as policymakers, decisionmakers, journalists and media organisations as to how they can mitigate and avoid the racialised rhetoric of an AI arms race between the United States and China.

In this article, we argue that facial emotion recognition technology (facial ERT) reproduces historical forms of pseudoscience based on the concept of quantifiable and unequally distributed emotional capacity. Drawing on Kyla Schuller’s Biopolitics of Feeling and Colin Koopman’s theory of infopower, we put forward the term ‘the infopolitics of feeling’ to describe how facial ERT encodes culturally ‘correct’ or normative forms of emotional expression that have historically been used to define and delineate what it means to be human. To make this argument, we provide a close reading of Girl Decoded, the autobiography of Rana el Kaliouby, the founder and former CEO of the leading Emotion artificial intelligence (AI) firm Affectiva. Girl Decoded, we argue pits el Kaliouby herself – portrayed as the empathetic, liberal, emotionally expressive and ideal ‘feeling’ subject – against two non-normative figures: the unfeeling autist and the inscrutable Oriental who must be ‘cured’ through Affectiva’s facial ERT.

While tech workers are essential stakeholders in ethical artificial intelligence (AI) development and deployment, they are rarely consulted about their understanding of the development of ethical AI. In light of this, we present the findings of our 2020 to 2021 empirical research study in which we collected data from tech workers in a major AI company to better understand what they consider to be the most pressing ethical issues when developing AI-powered products. While there is a nascent body of literature that examines how AI ethics principles are operationalised on the ground, this study differs in that we explicitly draw on feminist insights to inform our analysis, and have put a particular focus on allowing the voices and narratives of tech workers to lead the work forward. Our study generated three main findings: first, the term ‘bias’ creates real confusion among tech workers, meaning that the term is unable to do the ethical work it is intended to do; second, tech workers do not necessarily see a relationship between diversity, equality and inclusion (DEI) agendas and AI development, undermining AI ethics initiatives; and third, tech workers were particularly concerned about the monitoring and maintenance of unwieldy ‘legacy systems’ that generated serious challenges to creating and deploying new and more ethical AI products. This study thus creates a ‘thicker’ and more nuanced picture of tech workers’ perspectives on the ethical issues that arise when developing and maintaining AI systems, while simultaneously demonstrating the utility of feminist approaches in the field of AI ethics.

Responsibility has become a central concept in AI ethics; however, little research has been conducted into practitioners’ personal understandings of responsibility in the context of AI, including how responsibility should be defined and who is responsible when something goes wrong. In this article, we present findings from a 2020–2021 data set of interviews with AI practitioners and tech workers at a single multinational technology company and interpret them through the lens of feminist political thought. We reimagine responsibility in the context of AI development and deployment as the product of work cultures that enable tech workers to be responsive and answerable for their products over the long and short term. From our interviews, we identify three key pain points in understanding the distribution of responsibility between actors and developing responsible design and deployment practices: (1) unstable business ecosystems and AI lifecycles, which require an approach to responsibility that accounts for the dynamic nature of these systems; (2) the issue of incentivizing engineers to take responsibility for the mundane maintenance practices essential to the functioning of AI systems and (3) the need to overcome individual and structural barriers to taking ownership over AI products and their effects. From these findings, we make three recommendations based on feminist theory: (1) organisations should move from a static model of responsibility to a dynamic and ethically motivated response-ability; (2) companies need to revalue care and maintenance practices; and (3) firms must move away from individualistic ideas of responsibility towards fostering wider cultures of responsibility.

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.

In this paper, we analyze two key claims offered by recruitment AI companies in relation to the development and deployment of AI-powered HR tools: (1) recruitment AI can objectively assess candidates by removing gender and race from their systems, and (2) this removal of gender and race will make recruitment fairer, help customers attain their DEI goals, and lay the foundations for a truly meritocratic culture to thrive within an organization. We argue that these claims are misleading for four reasons: First, attempts to “strip” gender and race from AI systems often misunderstand what gender and race are, casting them as isolatable attributes rather than broader systems of power. Second, the attempted outsourcing of “diversity work” to AI-powered hiring tools may unintentionally entrench cultures of inequality and discrimination by failing to address the systemic problems within organizations. Third, AI hiring tools’ supposedly neutral assessment of candidates’ traits belie the power relationship between the observer and the observed. Specifically, the racialized history of character analysis and its associated processes of classification and categorization play into longer histories of taxonomical sorting and reflect the current demands and desires of the job market, even when not explicitly conducted along the lines of gender and race. Fourth, recruitment AI tools help produce the “ideal candidate” that they supposedly identify through by constructing associations between words and people’s bodies. From these four conclusions outlined above, we offer three key recommendations to AI HR firms, their customers, and policy makers going forward.