# Can LLMs Hire Fairly? Racial Bias in Resume Screening

arXiv: https://arxiv.org/abs/2606.28978 · HTML: https://mlsanity.com/paper/2606.28978/

## 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 (most cited first, 3 of 3)

- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPT](https://mlsanity.com/paper/2310.05135/) (2023)

## Built on by (most cited first, 4 of 4)

- [Proxy reliance in large language model decisions is uncalibrated to predictive evidence](https://mlsanity.com/paper/2608.22887/) (2026)
- [The Audit Decides the Verdict: Instrument Effects Rival Demographic Bias in LLM Decision Audits](https://mlsanity.com/paper/2609.09048/) (2026)
- [Beyond the Name: Demographic Leakage in De-Identified R'esum'es and Evaluation Artifacts in LLM Bias Audits](https://mlsanity.com/paper/2609.16501/) (2026)
- [Competence-Preserving Resume Perturbations Expose Presentation Sensitivity in LLM Screening](https://mlsanity.com/paper/2609.16517/) (2026)

## 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.