Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager

• URL: http://arxiv.org/abs/2604.00011v1
• Date: Tue, 10 Mar 2026 00:27:00 GMT
• Title: Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager
• Abstract summary: We quantify the degree to which large language models perpetuate societal biases and investigate prompt engineering as a bias mitigation technique.<n>Our findings suggest that for a given resumé, an LLM is more likely to hire a female candidate and perceive them as more qualified, but still recommends lower pay relative to male candidates.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing prominence of large language models (LLMs) in daily life has heightened concerns that LLMs exhibit many of the same gender-related biases as their creators. In the context of hiring decisions, we quantify the degree to which LLMs perpetuate societal biases and investigate prompt engineering as a bias mitigation technique. Our findings suggest that for a given resumé, an LLM is more likely to hire a female candidate and perceive them as more qualified, but still recommends lower pay relative to male candidates.

Related papers

• Who Gets Cited? Gender- and Majority-Bias in LLM-Driven Reference Selection [0.16317061277456998]
  This study systematically investigates gender bias in large language models (LLMs)<n>Our results reveal two forms of bias: a persistent preference for male-authored references and a majority-group bias that favors whichever gender is more prevalent in the candidate pool.<n>Our findings indicate that LLMs can reinforce or exacerbate existing gender imbalances in scholarly recognition.
  arXiv  Detail & Related papers  (2025-08-02T13:27:32Z)
• Gender and Positional Biases in LLM-Based Hiring Decisions: Evidence from Comparative CV/Résumé Evaluations [0.0]
  This study examines the behavior of Large Language Models (LLMs) when evaluating professional candidates based on their resumes or curricula vitae (CVs)<n>All LLMs consistently favored female-named candidates across 70 different professions.<n>Adding an explicit gender field (male/female) to the CVs further increased the preference for female applicants.
  arXiv  Detail & Related papers  (2025-05-16T06:19:35Z)
• Who Gets the Callback? Generative AI and Gender Bias [0.030693357740321777]
  We find that large language models (LLMs) tend to favor men, especially for higher-wage roles.<n>A comprehensive analysis of linguistic features in job ads reveals strong alignment of model recommendations with traditional gender stereotypes.<n>Our findings highlight how AI-driven hiring may perpetuate biases in the labor market and have implications for fairness and diversity within firms.
  arXiv  Detail & Related papers  (2025-04-30T07:55:52Z)
• Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs) [82.57490175399693]
  We study gender bias in 22 popular image-to-text vision-language assistants (VLAs)<n>Our results show that VLAs replicate human biases likely present in the data, such as real-world occupational imbalances.<n>To eliminate the gender bias in these models, we find that fine-tuning-based debiasing methods achieve the best trade-off between debiasing and retaining performance.
  arXiv  Detail & Related papers  (2024-10-25T05:59:44Z)
• GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models [73.23743278545321]
  Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but have also been observed to magnify societal biases.<n>GenderCARE is a comprehensive framework that encompasses innovative Criteria, bias Assessment, Reduction techniques, and Evaluation metrics.
  arXiv  Detail & Related papers  (2024-08-22T15:35:46Z)
• White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMs [58.27353205269664]
  Social biases can manifest in language agency in Large Language Model (LLM)-generated content.<n>We introduce the Language Agency Bias Evaluation benchmark, which comprehensively evaluates biases in LLMs.<n>Using LABE, we unveil language agency social biases in 3 recent LLMs: ChatGPT, Llama3, and Mistral.
  arXiv  Detail & Related papers  (2024-04-16T12:27:54Z)
• Disclosure and Mitigation of Gender Bias in LLMs [64.79319733514266]
  Large Language Models (LLMs) can generate biased responses. We propose an indirect probing framework based on conditional generation. We explore three distinct strategies to disclose explicit and implicit gender bias in LLMs.
  arXiv  Detail & Related papers  (2024-02-17T04:48:55Z)
• Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation [64.79319733514266]
  Large Language Models (LLMs) can generate biased and toxic responses. We propose a conditional text generation mechanism without the need for predefined gender phrases and stereotypes.
  arXiv  Detail & Related papers  (2023-11-01T05:31:46Z)
• "Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters [97.11173801187816]
  Large Language Models (LLMs) have recently emerged as an effective tool to assist individuals in writing various types of content. This paper critically examines gender biases in LLM-generated reference letters.
  arXiv  Detail & Related papers  (2023-10-13T16:12:57Z)
• Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie [2.1756081703276]
  We conducted a content analysis of social media discussions to gauge public perceptions of gender bias in large language models. People shared both observations of gender bias in their personal use and scientific findings about gender bias in LLMs. We propose governance recommendations to regulate gender bias in LLMs.
  arXiv  Detail & Related papers  (2023-09-17T00:53:34Z)
• Gender bias and stereotypes in Large Language Models [0.6882042556551611]
  This paper investigates Large Language Models' behavior with respect to gender stereotypes. We use a simple paradigm to test the presence of gender bias, building on but differing from WinoBias. Our contributions in this paper are as follows: (a) LLMs are 3-6 times more likely to choose an occupation that stereotypically aligns with a person's gender; (b) these choices align with people's perceptions better than with the ground truth as reflected in official job statistics; (d) LLMs ignore crucial ambiguities in sentence structure 95% of the time in our study items, but when explicitly prompted, they recognize
  arXiv  Detail & Related papers  (2023-08-28T22:32:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.