Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager
This addresses bias concerns in AI hiring tools, which is critical for fairness in employment, but it is incremental as it builds on existing research on LLM biases.
The study quantified gender bias in large language models (LLMs) during hiring decisions, finding that while LLMs were more likely to hire female candidates and perceive them as more qualified, they still recommended lower pay for females compared to males.
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.