mlsanityData to 14 Oct 2026

Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPT

arXiv 2310.05135 ↗First posted 8 Oct 2023cs.CL cs.AI cs.LGcited by 24

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Abstract (arXiv)

Large Language Models (LLMs) such as GPT-3.5, Bard, and Claude exhibit applicability across numerous tasks. One domain of interest is their use in algorithmic hiring, specifically in matching resumes with job categories. Yet, this introduces issues of bias on protected attributes like gender, race and maternity status. The seminal work of Bertrand & Mullainathan (2003) set the gold-standard for identifying hiring bias via field experiments where the response rate for identical resumes that differ only in protected attributes, e.g., racially suggestive names such as Emily or Lakisha, is compared. We replicate this experiment on state-of-art LLMs (GPT-3.5, Bard, Claude and Llama) to evaluate bias (or lack thereof) on gender, race, maternity status, pregnancy status, and political affiliation. We evaluate LLMs on two tasks: (1) matching resumes to job categories; and (2) summarizing resumes with employment relevant information. Overall, LLMs are robust across race and gender. They differ in their performance on pregnancy status and political affiliation. We use contrastive input decoding on open-source LLMs to uncover potential sources of bias.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 15 of its 36 references that are in the map. First posted on arXiv 8 Oct 2023.

Builds on

15 of 36 references.

References · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  5. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
  6. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 710
  7. 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 377
  8. 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 174
  9. 2021Mitigating Language-Dependent Ethnic Bias in BERTcited by 87
  10. 2020Deep Learning for Procedural Content Generationcited by 73
  11. 2021JobBERT: Understanding Job Titles through Skillscited by 29
  12. 2020Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Networkcited by 25
3 more
  1. 2021Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programmingcited by 24
  2. 2023Surfacing Biases in Large Language Models using Contrastive Input Decodingcited by 11
  3. 2017Domain Adaptation for Resume Classification Using Convolutional Neural Networkscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 9; 2025: 7; 2026: 6.

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  1. 2023Evaluating and Mitigating Discrimination in Language Model Decisionscited by 124
  2. 2024What's in a Name? Auditing Large Language Models for Race and Gender Biascited by 51
  3. 2024Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluationcited by 41
  4. 2024The Silicon Ceiling: Auditing GPT's Race and Gender Biases in Hiringcited by 29
  5. 2024Identifying and Improving Disability Bias in GPT-Based Resume Screeningcited by 28
  6. 2024Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender?cited by 25
  7. 2024Auditing the Use of Language Models to Guide Hiring Decisionscited by 20
  8. 2025Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Educationcited by 14
  9. 2024Social Debiasing for Fair Multi-modal LLMscited by 8
  10. 2025Rethinking LLM Bias Probing Using Lessons from the Social Sciencescited by 7
  11. 2025ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarioscited by 5
  12. 2026Can LLMs Hire Fairly? Racial Bias in Resume Screeningcited by 4
12 more
  1. 2024Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goalscited by 3
  2. 2025Understanding Gender Bias in AI-Generated Product Descriptionscited by 3
  3. 2023From Bytes to Biases: Investigating the Cultural Self-Perception of Large Language Modelscited by 1
  4. 2025Small Changes, Large Consequences: Analyzing the Allocational Fairness of LLMs in Hiring Contextscited by 1
  5. 2026Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Managercited by 1
  6. 2024Cheap Learning: Maximising Performance of Language Models for Social Data Science Using Minimal Datacited by 0
  7. 2025Name of Thrones: Evaluating How LLMs Rank Student Names, Race, and Gender in Status Hierarchiescited by 0
  8. 2025From job titles to jawlines: Using context voids to study generative AI systemscited by 0
  9. 2026AutoScreen-FW: An LLM-based Framework for Resume Screeningcited by 0
  10. 2026Decisive: Guiding User Decisions with Optimal Preference Elicitation from Unstructured Documentscited by 0
  11. 2026Moral Safety in LLMs: Exposing Performative Compliance with Puzzled Cuescited by 0
  12. 2026Thin Evidence, Thick Priors: How Language Models Substitute Identity for Missing Financial Factscited by 0