Hiring in the age of AI

This chapter explores persistent gender biases in hiring practices in the age of AI. Despite regulations against workforce gender discrimination, biases continue to manifest at various stages of hiring, often favoring men over equally qualified women. The concept of the “ideal worker,” which prioritizes work above personal commitments, is inherently gendered, traditionally associating men with full-time paid work and women with unpaid domestic responsibilities. The chapter explores how AI, while promising efficiency and impartiality, can perpetuate these biases if trained on historically biased data. It highlights the case of Amazon’s hiring algorithm, which filtered out CVs with women-identifying words. The chapter also discusses how gender-coded language in CVs influences hiring outcomes, with masculine-coded CVs receiving significantly more positive responses than feminine-coded ones. The implications of AI gender bias are profound, as it can reinforce existing inequalities, hinder women’s career advancement and perpetuate male-dominated leadership structures. The chapter concludes with recommendations for companies, policymakers and others to audit AI systems, craft gender-neutral job descriptions and promote diverse leadership models to mitigate these biases and inherent inequalities in the workplace and beyond.