Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager
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| Format: | Preprint |
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2026
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| _version_ | 1866914435731292160 |
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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 |
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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 |