Artificialising whiteness? How AI normalises whiteness in theory, policy and practice

Leonard, Pauline (2023) Artificialising whiteness? How AI normalises whiteness in theory, policy and practice. In, Andreassen, Rikke, Lundstrom, Catrin, Keskinen, Suvi and Tate, Shirley Anne (eds.) The Routledge International Handbook of New Critical Race and Whiteness Studies. 1st ed. Taylor & Francis, pp. 44-58.

Abstract

This chapter analyses artificial intelligence (AI) from perspectives of critical race and whiteness theory, socio-technical studies and governmentality. In her chapter ‘Artificializing whiteness? How AI normalises whiteness in theory, policy and practice’, Leonard shows how contemporary operationalisations of AI, such as automated decision making (ADM) processes, can be understood as a tool securing the privileges and power of whiteness, despite being positioned under the guise of neutral technology. Investigating how these racialising processes also intersect with gender and social class, Leonard shows how ADM systems ‘artificialise whiteness’ by consistently and routinely disadvantaging members of marginalised groups . . The chapter demonstrates how minorities, especially Black and ethnic minority women, are at risk of being subjected to unfair and biased (automated) decision making. She concludes that the US-dominated AI industry and many of AI’s design decisions collude in artificialising whiteness. She therefore advocates in favour of recognising AI as a racial, gendered and classist technology of governance which seeks to reassert, renew and normalise different facets of white privilege.

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Author: Pauline Leonard

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