@WGOV: Algorithmic Monocultures in Hiring Rishi Bommasani, Sarah H. Bana, Kathleen A. Creel, Dan Jurafsky, Percy Liang https:/…
Summary
A research paper analyzing how algorithmic monoculture in hiring—where many employers use the same vendor's screening algorithms—leads to systematic rejection of the same individuals and racial groups, using a dataset of 3 million applicants.
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Algorithmic Monocultures in Hiring
Rishi Bommasani, Sarah H. Bana, Kathleen A. Creel, Dan Jurafsky, Percy Liang https://t.co/wKsoz1luow [𝚌𝚜.𝙲𝚈 𝚌𝚜.𝙰𝙸] https://t.co/BkhfbvRAMg
Algorithmic Monocultures in Hiring
Source: https://arxiv.org/abs/2605.27371 View PDF
Abstract:Many employers screen job applicants with algorithms built by the same few algorithm vendors. We hypothesize that algorithmic monoculture leads to the same individuals and members of the same racial groups facing rejection. We acquire and analyze a novel dataset of 3 million applicants submitting 4 million applications where all the applications are screened by algorithms built by the same vendor. We find clear racial disparities in applicant outcomes. Of all applications submitted by Asian and Black applicants, 14.74% and 25.87% are submitted to positions that adversely impact Asian and Black applicants, respectively, according to U.S. employment discrimination standards. Individuals also receive homogeneous outcomes: 4% of all applicants who apply to 10 positions are recommended for rejection from all positions, a rate higher than expected by chance. To better understand this homogeneity, we leverage the deterministic replicability of hiring algorithms to generate the outcomes applicants would have received if they applied to all positions. We show that applicants would need to apply widely in order to ensure their applications are considered by a human
Submission history
From: Rishi Bommasani [view email] **[v1]**Tue, 26 May 2026 17:59:55 UTC (1,092 KB)
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