Can LLMs Hire Fairly? Racial Bias in Resume Screening
Zhenyu Gao, Wenxi Jiang, Yutong Yan
cs.CL cs.CY
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.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.