mlsanityData to 14 Oct 2026

Can LLMs Hire Fairly? Racial Bias in Resume Screening

arXiv 2606.28978 ↗First posted 27 Jun 2026cs.CL cs.CYcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 3 of its 7 references that are in the map. First posted on arXiv 27 Jun 2026.

Builds on

3 of 7 references.

References · 3

  1. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
  2. 2023Bias and Fairness in Large Language Models: A Surveycited by 639
  3. 2023Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPTcited by 24

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Proxy reliance in large language model decisions is uncalibrated to predictive evidencecited by 0
  2. 2026The Audit Decides the Verdict: Instrument Effects Rival Demographic Bias in LLM Decision Auditscited by 0
  3. 2026Beyond the Name: Demographic Leakage in De-Identified R'esum'es and Evaluation Artifacts in LLM Bias Auditscited by 0
  4. 2026Competence-Preserving Resume Perturbations Expose Presentation Sensitivity in LLM Screeningcited by 0