Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager
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.
| Authors | Nina Gerszberg, Janka Hamori, Andrew Lo |
|---|---|
| Primary category | cs.CY |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.