mlsanityData to 7 Oct 2026

Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education

arXiv 2503.19182 ↗First posted 24 Mar 2025cs.CLcited by 13

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Abstract (arXiv)

Large Language Models (LLMs) offer the potential to automate hiring by matching job descriptions with candidate resumes, streamlining recruitment processes, and reducing operational costs. However, biases inherent in these models may lead to unfair hiring practices, reinforcing societal prejudices and undermining workplace diversity. This study examines the performance and fairness of LLMs in job-resume matching tasks within the English language and U.S. context. It evaluates how factors such as gender, race, and educational background influence model decisions, providing critical insights into the fairness and reliability of LLMs in HR applications. Our findings indicate that while recent models have reduced biases related to explicit attributes like gender and race, implicit biases concerning educational background remain significant. These results highlight the need for ongoing evaluation and the development of advanced bias mitigation strategies to ensure equitable hiring practices when using LLMs in industry settings.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 20 of its 43 references that are in the map. First posted on arXiv 24 Mar 2025.

Builds on

20 of 43 references.

References · 20

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  3. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  4. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  5. 2023Large Language Modelscited by 774
  6. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 737
  7. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
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  11. 2024What's in a Name? Auditing Large Language Models for Race and Gender Biascited by 50
  12. 2023Fairness and Bias in Algorithmic Hiring: a Multidisciplinary Surveycited by 30
8 more
  1. 2023Less is More for Long Document Summary Evaluation by LLMscited by 28
  2. 2021Intersectional Bias in Causal Language Modelscited by 24
  3. 2023Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPTcited by 23
  4. 2024Auditing the Use of Language Models to Guide Hiring Decisionscited by 19
  5. 2023A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analyticscited by 17
  6. 2021Machamp: A Generalized Entity Matching Benchmarkcited by 16
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 11.

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  1. 2025Robustly Improving LLM Fairness in Realistic Settings via Interpretabilitycited by 16
  2. 2026Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMscited by 6
  3. 2026Counterfactual Fairness Evaluation of LLM-Based Contact Center Agent Quality Assurance Systemcited by 2
  4. 2026PopResume: Causal Fairness Evaluation of LLM/VLM Resume Screeners with Population-Representative Datasetcited by 1
  5. 2026Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiringcited by 1
  6. 2025MSLEF: Multi-Segment LLM Ensemble Finetuning in Recruitmentcited by 0
  7. 2026Synapse: Evolving Job-Person Fit with Explainable Two-phase Retrieval and LLM-guided Genetic Resume Optimizationcited by 0
  8. 2026Moral Safety in LLMs: Exposing Performative Compliance with Puzzled Cuescited by 0
  9. 2026Institutional Prestige as Geographic Bias in Large Language Models: Evidence from Three Factorial Experiments with Bootstrap Confidence Intervalscited by 0
  10. 2026Beyond Outcome Gaps: Process-Aware Fairness Diagnosis for LLM-based Multi-Agent Decision Systemscited by 0
  11. 2026Beyond the Name: Demographic Leakage in De-Identified R'esum'es and Evaluation Artifacts in LLM Bias Auditscited by 0
  12. 2026Competence-Preserving Resume Perturbations Expose Presentation Sensitivity in LLM Screeningcited by 0
1 more
  1. 2026When Hiring Becomes Agent-Mediated: Evaluating Access and Recurrence in Two-Agent R'esum'e Screeningcited by 0