mlsanityData to 7 Oct 2026

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

arXiv 2406.10486 ↗First posted 15 Jun 2024cs.CLcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 27 of its 62 references that are in the map. First posted on arXiv 15 Jun 2024.

Builds on

27 of 62 references.

References · 27

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,122
  5. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  6. 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 911
  7. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  8. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 737
  9. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 703
  10. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 683
  11. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
  12. 2022Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studiescited by 506
15 more
  1. 2018Gender Bias in Coreference Resolutioncited by 438
  2. 2023Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Modelscited by 204
  3. 2023"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letterscited by 196
  4. 2023Evaluating and Mitigating Discrimination in Language Model Decisionscited by 117
  5. 2019It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitutioncited by 102
  6. 2024What's in a Name? Auditing Large Language Models for Race and Gender Biascited by 50
  7. 2023MISGENDERED: Limits of Large Language Models in Understanding Pronounscited by 43
  8. 2020"You are grounded!": Latent Name Artifacts in Pre-trained Language Modelscited by 39
  9. 2021Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Modelscited by 39
  10. 2023Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPTcited by 23
  11. 2022SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Modelscited by 22
  12. 2021What do Large Language Models Learn about Scripts?cited by 19
  13. 2024Auditing the Use of Language Models to Guide Hiring Decisionscited by 19
  14. 2023Nichelle and Nancy: The Influence of Demographic Attributes and Tokenization Length on First Name Biasescited by 12
  15. 2023Examining the Causal Effect of First Names on Language Models: The Case of Social Commonsense Reasoningcited by 8

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Citing papers per year: 2024: 5; 2025: 10; 2026: 9.

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  1. 2024Bias in Large Language Models: Origin, Evaluation, and Mitigationcited by 77
  2. 2025Towards Understanding the Safety Boundaries of DeepSeek Models: Evaluation and Findingscited by 24
  3. 2024Gender Bias in LLM-generated Interview Responsescited by 10
  4. 2025Addressing Bias in Generative AI: Challenges and Research Opportunities in Information Managementcited by 7
  5. 2024Attention Speaks Volumes: Localizing and Mitigating Bias in Language Modelscited by 6
  6. 2024Understanding the Effects of Human-written Paraphrases in LLM-generated Text Detectioncited by 6
  7. 2025More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Modelscited by 5
  8. 2025On the Effectiveness and Generalization of Race Representations for Debiasing High-Stakes Decisionscited by 5
  9. 2025Rethinking Prompt-based Debiasing in Large Language Modelscited by 4
  10. 2025Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolutioncited by 3
  11. 2024LLM Bias Evaluation: Gender, Racial, and Age Disparities in Occupational and Crime Scenarioscited by 2
  12. 2025Social Welfare Function Leaderboard: When LLM Agents Allocate Social Welfarecited by 2
12 more
  1. 2025Camellia: Benchmarking Cultural Biases in LLMs for Asian Languagescited by 2
  2. 2025A word association network methodology for evaluating implicit biases in LLMs compared to humanscited by 2
  3. 2026Nationality and Region Prediction from Names: A Comparative Study of Neural Models and Large Language Modelscited by 1
  4. 2026How Implicit Bias Accumulates and Propagates in LLM Long-term Memorycited by 1
  5. 2025Gender-Neutral Large Language Models for Medical Applications: Reducing Bias in PubMed Abstractscited by 0
  6. 2026Who Does This Name Remind You of ? Nationality Prediction via Large Language Model Associative Memorycited by 0
  7. 2026Side-by-side Comparison Amplifies Dialect Bias in Language Modelscited by 0
  8. 2026Status Association Does Not Reliably Predict Decision Leakagecited by 0
  9. 2026Institutional Prestige as Geographic Bias in Large Language Models: Evidence from Three Factorial Experiments with Bootstrap Confidence Intervalscited by 0
  10. 2026Proxy reliance in large language model decisions is uncalibrated to predictive evidencecited by 0
  11. 2026Counterfactual Bias Testing for Application Tracking Systemcited by 0
  12. 2026Who Gets a Token, and What Does It Carry? Unequal Name Support and Concept Access in Large Language Modelscited by 0