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

Fuente: arXiv
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Main Authors: Gerszberg, Nina, Hamori, Janka, Lo, Andrew
Format: Preprint
Published: 2026
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author Gerszberg, Nina
Hamori, Janka
Lo, Andrew
author_facet Gerszberg, Nina
Hamori, Janka
Lo, Andrew
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2604_00011
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager
Gerszberg, Nina
Hamori, Janka
Lo, Andrew
Computers and Society
Artificial Intelligence
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.
title Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager
topic Computers and Society
Artificial Intelligence
url https://arxiv.org/abs/2604.00011