New Accelerants of Inequality Regimes: AI‐Hiring Tools and Algorithmic Bias

Abstract

AI‐hiring technology is becoming increasingly widely adopted, raising important questions about what effect such technology may have on organizational inequality regimes. Although there is a large and growing body of literature surrounding the effects of AI hiring, its impacts, and bias, markedly less is known about who develops, sells, and markets such tools, and what role this may play in the altering and further entrenching of inequality regimes. Drawing on the voices of 21 developers and hiring platform vendors, this study explores current levels of apathy surrounding gender equality in AI‐hiring tech. From the findings, it is argued that DEI issues and subsequent debiasing are seen as iterative, retrofit solutions, problems that can be tended to retrospectively, rather than inclusion and equity being built‐in from the outset. In turn, it is posited that algorithmic bias and AI‐hiring tools serve as a contemporary facet of inequality regimes. This article offers a threefold contribution to Feminist AI scholarship: a theoretical extension to Acker's inequality regimes framework in the form of an annex, which includes the role of artificial intelligence in the reproduction of inequalities, a conceptual model which outlines new accelerants of inequality regimes, and contemporary empirical insights from a hard to access group.