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

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arxiv 2406.10486 v1 pith:TU7GM4PT submitted 2024-06-15 cs.CL

Haozhe An , Christabel Acquaye , Colin Wang , Zongxia Li , Rachel Rudinger This is my paper · ORCID
classification cs.CL
keywords hiringllmsacceptancedecisionsapplicantapplicantsemailethnicity
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Who Endorsed It? Measuring Authority Bias Across Expertise Levels in Language Models

    cs.CL 2026-01 reject novelty 6.0 of 10

    Language models accept misleading endorsements more readily from higher-expertise persona sources, and the effect can be partially steered away by subtracting a residual-stream vector.

  2. Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages

    cs.CL 2025-10 conditional novelty 6.0 of 10

    Across nine Asian languages, multilingual LLMs favor Western cultural entities in 30-40% of culturally grounded contexts, with model-specific sentiment biases and extraction accuracy gaps.

  3. Better Together: Quantifying the Benefits of AI-Assisted Recruitment

    cs.CL 2025-07 reject novelty 6.0 of 10

    Candidates routed through an AI video-interview pipeline passed a blind final human interview at 54%, versus 34% for resume-screened candidates, a 20-point gap that is imprecise and rests on only 70 finalists.

  4. Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution

    cs.CL 2025-08 unverdicted novelty 5.0 of 10

    The abstract claims LLMs show up to 40% coreference confidence disparities across intersectional identities, but the article body is an unrelated paper on robotic fruit handling.

  5. Towards Experience-Centered AI: A Framework for Integrating Lived Experience in Design and Development

    cs.CY 2025-08 conditional novelty 5.0 of 10

    A conceptual framework, LEAF, organizes four dimensions of lived experience into a five-stage AI development pipeline for more human-centered systems.

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