Can LLMs Hire Fairly? Racial Bias in Resume Screening

Zhenyu Gao; Wenxi Jiang; Yutong Yan

cs.CLJun 27, 2026

Can LLMs Hire Fairly? Racial Bias in Resume Screening

Authors: Zhenyu Gao, Wenxi Jiang, Yutong Yan

Organizations: Department of Finance, CUHK Business School, The Chinese University of Hong Kong

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.

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Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring

Huy Nghiem, Phuong-Anh Nguyen-Le, Sy-Tuyen Ho, Hal Daume

University of Maryland

Research has documented LLMs' name-based bias in hiring and salary recommendations. In this paper, we instead consider a setting where LLMs generate candidate summaries for downstream assessment. In a large-scale controlled study, we analyze nearly one million resume summaries produced by 4 models under systematic race-gender name perturbations, using synthetic resumes and real-world job postings. By decomposing each summary into resume-grounded factual content and evaluative framing, we find that factual content remains largely stable, while evaluative language exhibits subtle name-conditioned variation concentrated in the extremes of the distribution, especially in open-source models. Our hiring simulation demonstrates how evaluative summary transforms directional harm into symmetric instability that might evade conventional fairness audit, highlighting a potential pathway for LLM-to-LLM automation bias.

Large Language Model Bias Implicit Bias Summarization Task Framing

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Abstract

Explore similar work

Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring

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PopResume: Causal Fairness Evaluation of LLM/VLM Resume Screeners with Population-Representative Dataset