Algorithmic Monocultures in Hiring: When One Vendor's Bias Becomes Everyone's Problem

Abstract: Employment algorithms have rapidly scaled across labor markets, with major vendors processing millions of applications annually. This independent empirical analysis examines a novel dataset of 4.2 million job applications screened by a single algorithm vendor, revealing systematic patterns of adverse impact and outcome homogenization. Disaggregated position-level analysis demonstrates that 10.62% of roles show adverse impact against Black applicants and 5.32% against Asian applicants, despite vendor claims of aggregate fairness. Beyond group-level disparities, 4% of applicants applying to ten positions face rejection from all positions—a rate exceeding chance expectations. Comparison with the largest prior hiring study shows algorithmic screening produces qualitatively different labor market dynamics than traditional processes. These findings illuminate how vendor consolidation creates structural vulnerabilities: when employers share algorithmic infrastructure, discrimination at one firm predicts discrimination at another, and individual rejections become systemic exclusion. The research has immediate policy implications for employment discrimination enforcement, algorithmic accountability frameworks, and researcher access to deployed systems.

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