mlsanityData to 7 Oct 2026

"You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of Large Language Models in Employment Recommendations

arXiv 2406.12232 ↗First posted 18 Jun 2024cs.AI cs.CLcited by 33

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Abstract (arXiv)

Social science research has shown that candidates with names indicative of certain races or genders often face discrimination in employment practices. Similarly, Large Language Models (LLMs) have demonstrated racial and gender biases in various applications. In this study, we utilize GPT-3.5-Turbo and Llama 3-70B-Instruct to simulate hiring decisions and salary recommendations for candidates with 320 first names that strongly signal their race and gender, across over 750,000 prompts. Our empirical results indicate a preference among these models for hiring candidates with White female-sounding names over other demographic groups across 40 occupations. Additionally, even among candidates with identical qualifications, salary recommendations vary by as much as 5% between different subgroups. A comparison with real-world labor data reveals inconsistent alignment with U.S. labor market characteristics, underscoring the necessity of risk investigation of LLM-powered systems.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). Builds on 17 of its 48 references that are in the map. First posted on arXiv 18 Jun 2024.

Builds on

17 of 48 references.

References · 17

  1. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
  4. 2019Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Settingcited by 346
  5. 2023"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letterscited by 196
  6. 2023Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMscited by 191
  7. 2024Data-efficient Fine-tuning for LLM-based Recommendationcited by 103
  8. 2024What's in a Name? Auditing Large Language Models for Race and Gender Biascited by 50
  9. 2024Disclosure and Mitigation of Gender Bias in LLMscited by 47
  10. 2023The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendationscited by 41
  11. 2022Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanationscited by 40
  12. 2024PEDANTS: Cheap but Effective and Interpretable Answer Equivalencecited by 31
5 more
  1. 2024Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustmentcited by 29
  2. 2024Bias patterns in the application of LLMs for clinical decision support: A comprehensive studycited by 27
  3. 2024Prompting Fairness: Integrating Causality to Debias Large Language Modelscited by 20
  4. 2020Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utilitycited by 9
  5. 2024AI as Decision-Maker: Ethics and Risk Preferences of LLMscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 16; 2026: 15.

33 papers.

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  1. 2025Reading Between the Prompts: How Stereotypes Shape LLM's Implicit Personalizationcited by 24
  2. 2025Presumed Cultural Identity: How Names Shape LLM Responsescited by 21
  3. 2024Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Modelscited by 15
  4. 2025Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignmentcited by 14
  5. 2024Natural Language Processing for Human Resources: A Surveycited by 9
  6. 2025Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Taskscited by 9
  7. 2026One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalizationcited by 9
  8. 2025More of the Same: Persistent Representational Harms Under Increased Representationcited by 6
  9. 2025Language Models Predict Empathy Gaps Between Social In-groups and Out-groupscited by 6
  10. 2025Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinesecited by 6
  11. 2025Surface Fairness, Deep Bias: A Comparative Study of Bias in Language Modelscited by 6
  12. 2025More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Modelscited by 5
21 more
  1. 2025Evaluate Bias without Manual Test Sets: A Concept Representation Perspective for LLMscited by 5
  2. 2025On the Mutual Influence of Gender and Occupation in LLM Representationscited by 4
  3. 2025Rethinking Prompt-based Debiasing in Large Language Modelscited by 4
  4. 2026Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Biascited by 3
  5. 2025XToM: Exploring the Multilingual Theory of Mind for Large Language Modelscited by 2
  6. 2025Camellia: Benchmarking Cultural Biases in LLMs for Asian Languagescited by 2
  7. 2026Measuring Social Bias in Vision-Language Models with Face-Only Counterfactuals from Real Photoscited by 2
  8. 2026JobResQA: A Benchmark for LLM Machine Reading Comprehension on Multilingual R'esum'es and JDscited by 2
  9. 2026FairFund-Bench: Evaluating Distributive Bias in LLM Resource Allocationcited by 2
  10. 2025Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contextscited by 1
  11. 2025Evaluating Implicit Biases in LLM Reasoning through Logic Grid Puzzlescited by 1
  12. 2026PopResume: Causal Fairness Evaluation of LLM/VLM Resume Screeners with Population-Representative Datasetcited by 1
  13. 2026Topics as Proxies for Sociodemographics: How Conversational Context Affects LLM Answerscited by 1
  14. 2026STEREODISCO: Discovering Stereotypicality in LLMscited by 0
  15. 2026Status Association Does Not Reliably Predict Decision Leakagecited by 0
  16. 2026The Safeguard Worked. Is the LLM System Safer?cited by 0
  17. 2026The Audit Decides the Verdict: Instrument Effects Rival Demographic Bias in LLM Decision Auditscited by 0
  18. 2026Beyond the Name: Demographic Leakage in De-Identified R'esum'es and Evaluation Artifacts in LLM Bias Auditscited by 0
  19. 2026Monocultural Biases: Correlated biases in large language models lead to unequal systemic exclusion rates in hiringcited by 0
  20. 2026RupeeBias: Auditing Demographic Bias in Indian Economic Guidance from Large Language Modelscited by 0
  21. 2026Who Gets a Token, and What Does It Carry? Unequal Name Support and Concept Access in Large Language Modelscited by 0