Can LLMs Hire Fairly? Racial Bias in Resume Screening
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Computer Science > Computation and Language
Title:Can LLMs Hire Fairly? Racial Bias in Resume Screening
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.
| Subjects: | Computation and Language (cs.CL); Computers and Society (cs.CY) |
| Cite as: | arXiv:2606.28978 [cs.CL] |
| (or arXiv:2606.28978v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.28978
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