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.
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