# Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender?

arXiv: https://arxiv.org/abs/2406.10486 · HTML: https://mlsanity.com/paper/2406.10486/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 62 references that are in the map.
- First posted on arXiv 15 Jun 2024.

## Builds on (most cited first, 27 of 27)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity](https://mlsanity.com/paper/2104.08786/) (2021)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [BBQ: A Hand-Built Bias Benchmark for Question Answering](https://mlsanity.com/paper/2110.08193/) (2021)
- [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://mlsanity.com/paper/1804.06876/) (2018)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Gender Bias in Coreference Resolution](https://mlsanity.com/paper/1804.09301/) (2018)
- [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://mlsanity.com/paper/2305.18189/) (2023)
- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [Evaluating and Mitigating Discrimination in Language Model Decisions](https://mlsanity.com/paper/2312.03689/) (2023)
- [It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution](https://mlsanity.com/paper/1909.00871/) (2019)
- [What's in a Name? Auditing Large Language Models for Race and Gender Bias](https://mlsanity.com/paper/2402.14875/) (2024)
- [MISGENDERED: Limits of Large Language Models in Understanding Pronouns](https://mlsanity.com/paper/2306.03950/) (2023)
- ["You are grounded!": Latent Name Artifacts in Pre-trained Language Models](https://mlsanity.com/paper/2004.03012/) (2020)
- [Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models](https://mlsanity.com/paper/2110.00672/) (2021)
- [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)
- [SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models](https://mlsanity.com/paper/2210.07269/) (2022)
- [Auditing the Use of Language Models to Guide Hiring Decisions](https://mlsanity.com/paper/2404.03086/) (2024)
- [What do Large Language Models Learn about Scripts?](https://mlsanity.com/paper/2112.13834/) (2021)
- [Nichelle and Nancy: The Influence of Demographic Attributes and Tokenization Length on First Name Biases](https://mlsanity.com/paper/2305.16577/) (2023)
- [Examining the Causal Effect of First Names on Language Models: The Case of Social Commonsense Reasoning](https://mlsanity.com/paper/2306.01117/) (2023)

## Built on by (most cited first, 25 of 25)

- [Bias in Large Language Models: Origin, Evaluation, and Mitigation](https://mlsanity.com/paper/2411.10915/) (2024)
- [Towards Understanding the Safety Boundaries of DeepSeek Models: Evaluation and Findings](https://mlsanity.com/paper/2503.15092/) (2025)
- [Gender Bias in LLM-generated Interview Responses](https://mlsanity.com/paper/2410.20739/) (2024)
- [Addressing Bias in Generative AI: Challenges and Research Opportunities in Information Management](https://mlsanity.com/paper/2502.10407/) (2025)
- [Attention Speaks Volumes: Localizing and Mitigating Bias in Language Models](https://mlsanity.com/paper/2410.22517/) (2024)
- [Understanding the Effects of Human-written Paraphrases in LLM-generated Text Detection](https://mlsanity.com/paper/2411.03806/) (2024)
- [More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Models](https://mlsanity.com/paper/2503.15904/) (2025)
- [On the Effectiveness and Generalization of Race Representations for Debiasing High-Stakes Decisions](https://mlsanity.com/paper/2504.06303/) (2025)
- [Rethinking Prompt-based Debiasing in Large Language Models](https://mlsanity.com/paper/2503.09219/) (2025)
- [Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution](https://mlsanity.com/paper/2508.07111/) (2025)
- [LLM Bias Evaluation: Gender, Racial, and Age Disparities in Occupational and Crime Scenarios](https://mlsanity.com/paper/2409.14583/) (2024)
- [Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfare](https://mlsanity.com/paper/2510.01164/) (2025)
- [Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages](https://mlsanity.com/paper/2510.05291/) (2025)
- [A word association network methodology for evaluating implicit biases in LLMs compared to humans](https://mlsanity.com/paper/2510.24488/) (2025)
- [Nationality and Region Prediction from Names: A Comparative Study of Neural Models and Large Language Models](https://mlsanity.com/paper/2601.08692/) (2026)
- [How Implicit Bias Accumulates and Propagates in LLM Long-term Memory](https://mlsanity.com/paper/2602.01558/) (2026)
- [Gender-Neutral Large Language Models for Medical Applications: Reducing Bias in PubMed Abstracts](https://mlsanity.com/paper/2501.06365/) (2025)
- [Who Does This Name Remind You of ? Nationality Prediction via Large Language Model Associative Memory](https://mlsanity.com/paper/2601.12771/) (2026)
- [Side-by-side Comparison Amplifies Dialect Bias in Language Models](https://mlsanity.com/paper/2605.24384/) (2026)
- [Status Association Does Not Reliably Predict Decision Leakage](https://mlsanity.com/paper/2608.10089/) (2026)
- [Institutional Prestige as Geographic Bias in Large Language Models: Evidence from Three Factorial Experiments with Bootstrap Confidence Intervals](https://mlsanity.com/paper/2608.18107/) (2026)
- [Proxy reliance in large language model decisions is uncalibrated to predictive evidence](https://mlsanity.com/paper/2608.22887/) (2026)
- [Counterfactual Bias Testing for Application Tracking System](https://mlsanity.com/paper/2608.26899/) (2026)
- [Who Gets a Token, and What Does It Carry? Unequal Name Support and Concept Access in Large Language Models](https://mlsanity.com/paper/2609.34065/) (2026)
- [Causally Fair Generation with Large Language Models](https://mlsanity.com/paper/2610.04444/) (2026)

## Abstract (arXiv)

We examine whether large language models (LLMs) exhibit race- and gender-based name discrimination in hiring decisions, similar to classic findings in the social sciences (Bertrand and Mullainathan, 2004). We design a series of templatic prompts to LLMs to write an email to a named job applicant informing them of a hiring decision. By manipulating the applicant's first name, we measure the effect of perceived race, ethnicity, and gender on the probability that the LLM generates an acceptance or rejection email. We find that the hiring decisions of LLMs in many settings are more likely to favor White applicants over Hispanic applicants. In aggregate, the groups with the highest and lowest acceptance rates respectively are masculine White names and masculine Hispanic names. However, the comparative acceptance rates by group vary under different templatic settings, suggesting that LLMs' race- and gender-sensitivity may be idiosyncratic and prompt-sensitive.