Algorithmic Monocultures in Hiring

2026-05-26

AI Summary

This study addresses how algorithmic homogenization exacerbates racial disparities in hiring, disproportionately disadvantaging Asian and Black job seekers through systemic exclusion. Leveraging a dataset of 4 million real applications from 3 million applicants, the research provides the first empirical evidence of widespread “algorithmic monoculture” driven by reliance on a single algorithmic vendor across hiring platforms. Exploiting the deterministic and reproducible nature of these algorithms, the authors simulate each applicant’s outcomes across all available positions and evaluate fairness using U.S. employment discrimination standards. Findings reveal that 14.74% of Asian and 25.87% of Black applicants are systematically routed to less favorable roles, and 4% of job seekers receive no algorithmic recommendations after applying to ten positions—significantly exceeding random expectation. The work proposes that large-scale application strategies may be necessary to overcome algorithmic screening barriers.

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

Problem

• algorithmic monocultures
• hiring algorithms
• racial disparities
• employment discrimination
• applicant screening

Innovation

• algorithmic monoculture
• hiring algorithms
• racial disparity
• deterministic replicability
• employment discrimination