Algorithmic Monocultures in Hiring
2026-05-26 • Computers and Society
Computers and SocietyArtificial Intelligence
AI summaryⓘ
The authors studied how job application screening algorithms from a single vendor affect applicants. They found that these algorithms lead to racial disparities, with Asian and Black applicants more likely to be rejected unfairly. Many applicants get the same negative outcome across multiple job applications, showing a lack of variety in decisions. Their analysis suggests that applying to many jobs is necessary to have a chance of getting human review.
algorithmic screeningjob applicationsracial disparityalgorithmic monocultureemployment discriminationdeterministic algorithmshuman reviewapplicant outcomeshiring algorithms
Authors
Rishi Bommasani, Sarah H. Bana, Kathleen A. Creel, Dan Jurafsky, Percy Liang
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