# Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

arXiv: https://arxiv.org/abs/2407.20371 · HTML: https://mlsanity.com/paper/2407.20371/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research](https://mlsanity.com/paper/2402.00159/) (2024)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases](https://mlsanity.com/paper/2006.03955/) (2020)
- [Infini-gram: Scaling Unbounded n-gram Language Models to a Trillion Tokens](https://mlsanity.com/paper/2401.17377/) (2024)
- [Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models](https://mlsanity.com/paper/2102.04130/) (2021)
- [Capturing Failures of Large Language Models via Human Cognitive Biases](https://mlsanity.com/paper/2202.12299/) (2022)
- [ROBBIE: Robust Bias Evaluation of Large Generative Language Models](https://mlsanity.com/paper/2311.18140/) (2023)
- [The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions](https://mlsanity.com/paper/2310.12418/) (2023)
- [Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models](https://mlsanity.com/paper/2110.00672/) (2021)
- [American == White in Multimodal Language-and-Image AI](https://mlsanity.com/paper/2207.00691/) (2022)
- ['Person' == Light-skinned, Western Man, and Sexualization of Women of Color: Stereotypes in Stable Diffusion](https://mlsanity.com/paper/2310.19981/) (2023)
- [Towards a Holistic Approach: Understanding Sociodemographic Biases in NLP Models using an Interdisciplinary Lens](https://mlsanity.com/paper/2308.13089/) (2023)
- [Degendering Resumes for Fair Algorithmic Resume Screening](https://mlsanity.com/paper/2112.08910/) (2021)

## Built on by (most cited first, 30 of 51)

- [Mind the Value-Action Gap: Do LLMs Act in Alignment with Their Values?](https://mlsanity.com/paper/2501.15463/) (2025)
- [Correlated Errors in Large Language Models](https://mlsanity.com/paper/2506.07962/) (2025)
- [AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening](https://mlsanity.com/paper/2504.02870/) (2025)
- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [The Bias is in the Details: An Assessment of Cognitive Bias in LLMs](https://mlsanity.com/paper/2509.22856/) (2025)
- [No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomy](https://mlsanity.com/paper/2509.04404/) (2025)
- [Robustness and Confounders in the Demographic Alignment of LLMs with Human Perceptions of Offensiveness](https://mlsanity.com/paper/2411.08977/) (2024)
- [Evaluating the Promise and Pitfalls of LLMs in Hiring Decisions](https://mlsanity.com/paper/2507.02087/) (2025)
- [Intrinsic Bias is Predicted by Pretraining Data and Correlates with Downstream Performance in Vision-Language Encoders](https://mlsanity.com/paper/2502.07957/) (2025)
- [Invisible Filters: Cultural Bias in Hiring Evaluations Using Large Language Models](https://mlsanity.com/paper/2508.16673/) (2025)
- [Towards Effective Discrimination Testing for Generative AI](https://mlsanity.com/paper/2412.21052/) (2024)
- [Fragile Preferences: A Deep Dive Into Order Effects in Large Language Models](https://mlsanity.com/paper/2506.14092/) (2025)
- [On the Mutual Influence of Gender and Occupation in LLM Representations](https://mlsanity.com/paper/2503.06792/) (2025)
- [Gender and Positional Biases in LLM-Based Hiring Decisions: Evidence from Comparative CV/R'esum'e Evaluations](https://mlsanity.com/paper/2505.17049/) (2025)
- [Model Misalignment and Language Change: Traces of AI-Associated Language in Unscripted Spoken English](https://mlsanity.com/paper/2508.00238/) (2025)
- [Learning With Multi-Group Guarantees For Clusterable Subpopulations](https://mlsanity.com/paper/2410.14588/) (2024)
- [HInter: Exposing Hidden Intersectional Bias in Large Language Models](https://mlsanity.com/paper/2503.11962/) (2025)
- [The Impact of Disability Disclosure on Fairness and Bias in LLM-Driven Candidate Selection](https://mlsanity.com/paper/2506.00256/) (2025)
- [Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution](https://mlsanity.com/paper/2508.07111/) (2025)
- [Talent or Luck? Evaluating Attribution Bias in Large Language Models](https://mlsanity.com/paper/2505.22910/) (2025)
- [PJB: A Reasoning-Aware Benchmark for Person-Job Retrieval](https://mlsanity.com/paper/2603.17386/) (2026)
- [Locating and Controlling Implicit Personalization in Large Language Models](https://mlsanity.com/paper/2608.11735/) (2026)
- [Current State-of-the-Art of Bias Detection and Mitigation in Machine Translation for African and European Languages: a Review](https://mlsanity.com/paper/2410.21126/) (2024)
- [Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contexts](https://mlsanity.com/paper/2501.04316/) (2025)
- [Assessing instructor-AI cooperation for grading essay-type questions in an introductory sociology course](https://mlsanity.com/paper/2501.06461/) (2025)
- [GenderBench: Evaluation Suite for Gender Biases in LLMs](https://mlsanity.com/paper/2505.12054/) (2025)
- [JustEva: A Toolkit to Evaluate LLM Fairness in Legal Knowledge Inference](https://mlsanity.com/paper/2509.12104/) (2025)
- [Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs](https://mlsanity.com/paper/2509.16462/) (2025)
- [From Personalization to Prejudice: Bias and Discrimination in Memory-Enhanced AI Agents for Recruitment](https://mlsanity.com/paper/2512.16532/) (2025)
- [Identity-Robust Language Model Generation via Content Integrity Preservation](https://mlsanity.com/paper/2601.09141/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) hiring tools have revolutionized resume screening, and large language models (LLMs) have the potential to do the same. However, given the biases which are embedded within LLMs, it is unclear whether they can be used in this scenario without disadvantaging groups based on their protected attributes. In this work, we investigate the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection. Using that framework, we then perform a resume audit study to determine whether a selection of Massive Text Embedding (MTE) models are biased in resume screening scenarios. We simulate this for nine occupations, using a collection of over 500 publicly available resumes and 500 job descriptions. We find that the MTEs are biased, significantly favoring White-associated names in 85.1% of cases and female-associated names in only 11.1% of cases, with a minority of cases showing no statistically significant differences. Further analyses show that Black males are disadvantaged in up to 100% of cases, replicating real-world patterns of bias in employment settings, and validate three hypotheses of intersectionality. We also find an impact of document length as well as the corpus frequency of names in the selection of resumes. These findings have implications for widely used AI tools that are automating employment, fairness, and tech policy.