Can LLMs Hire Fairly? Racial Bias in Resume Screening

Zhenyu Gao; Wenxi Jiang; Yutong Yan

Can LLMs Hire Fairly? Racial Bias in Resume Screening

Computation and Language 2026-06-27 v1 Computers and Society

Authors: Zhenyu Gao , Wenxi Jiang , Yutong Yan

Abstract

We audit fourteen mainstream large language models (LLMs) for hiring discrimination using the paired-resume methodology of Kline, Rose, and Walters (2022). The sole 2023-vintage model reproduces the pro-White callback gap documented in field experiments on labor market discrimination ( $+2.12$ pp, significant at the 1\% level). Every model released in 2024 or after shows either a null gap or a significant pro-Black reversal (up to $-3.01$ pp). The same pattern holds on the gender axis. Based on 24,024 paired postings per model across 14 models, our results document a reversal in the direction of algorithmic hiring bias across model generations.

Keywords

algorithmic hiring bias large language models resume screening racial bias audit study model generations

Cite

@article{arxiv.2606.28978,
  title  = {Can LLMs Hire Fairly? Racial Bias in Resume Screening},
  author = {Zhenyu Gao and Wenxi Jiang and Yutong Yan},
  journal= {arXiv preprint arXiv:2606.28978},
  year   = {2026}
}