# FAIRE: Assessing Racial and Gender Bias in AI-Driven Resume Evaluations

arXiv: https://arxiv.org/abs/2504.01420 · HTML: https://mlsanity.com/paper/2504.01420/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search](https://mlsanity.com/paper/1905.01989/) (2019)
- [Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Models](https://mlsanity.com/paper/2408.03907/) (2024)
- [Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening](https://mlsanity.com/paper/2401.08315/) (2024)
- [Large Language Models Still Exhibit Bias in Long Text](https://mlsanity.com/paper/2410.17519/) (2024)

## Built on by (most cited first, 5 of 5)

- [Fragile Preferences: A Deep Dive Into Order Effects in Large Language Models](https://mlsanity.com/paper/2506.14092/) (2025)
- [CoMAI: A Collaborative Multi-Agent Framework for Robust and Equitable Interview Evaluation](https://mlsanity.com/paper/2603.16215/) (2026)
- [Identifying Harm in Personalized, Generative AI Systems Requires User-Centered Auditing at the Interaction Level](https://mlsanity.com/paper/2608.14692/) (2026)
- [Epistemic Warrant for LLM Recommendations: Characterizing the Basis for Reliance When Ground Truth Is Unavailable](https://mlsanity.com/paper/2609.04127/) (2026)
- [From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance](https://mlsanity.com/paper/2609.04286/) (2026)

## Abstract (arXiv)

In an era where AI-driven hiring is transforming recruitment practices, concerns about fairness and bias have become increasingly important. To explore these issues, we introduce a benchmark, FAIRE (Fairness Assessment In Resume Evaluation), to test for racial and gender bias in large language models (LLMs) used to evaluate resumes across different industries. We use two methods-direct scoring and ranking-to measure how model performance changes when resumes are slightly altered to reflect different racial or gender identities. Our findings reveal that while every model exhibits some degree of bias, the magnitude and direction vary considerably. This benchmark provides a clear way to examine these differences and offers valuable insights into the fairness of AI-based hiring tools. It highlights the urgent need for strategies to reduce bias in AI-driven recruitment. Our benchmark code and dataset are open-sourced at our repository: https://github.com/athenawen/FAIRE-Fairness-Assessment-In-Resume-Evaluation.git.