Algorithmic Monocultures in Hiring

Authors

• Rishi Bommasani, Stanford UniversityFollow
• Sarah H. Bana, Chapman UniversityFollow
• Kathleen A. Creel, Northeastern UniversityFollow
• Dan Saul Jurafsky, jurafsky@stanford.edu
• Percy Liang, Stanford UniversityFollow

Document Type

Conference Proceeding

Publication Date

6-25-2026

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.

Comments

This article was originally published in The 2026 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’26) in 2026. https://doi.org/10.1145/3805689.3812400

Recommended Citation

Rishi Bommasani, Sarah H. Bana, Kathleen A. Creel, Dan Jurafsky, and Percy Liang. 2026. Algorithmic Monocultures in Hiring. Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, New York, NY, USA, 6351–6382. https://doi.org/10.1145/3805689.3812400

Peer Reviewed

1

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.