测试发现大模型在招聘中偏爱女性但给更低薪酬
Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager
- 用简历模拟招聘，测试大模型性别偏好
- 女性简历获更高录用率但薪资推荐低15%
- 提示工程可缓解部分偏见，适合求职公平研究者
大型语言模型（LLMs）在日常生活中日益普及，引发了对其是否继承创作者性别偏见的担忧。本文研究了在招聘决策背景下，大模型如何体现社会偏见，并探讨提示工程作为缓解偏见的技术。结果表明，对于同一份简历，大模型更倾向于聘用女性候选人并认为其更胜任，但仍会推荐比男性低15%的薪资。这揭示了模型在录用与薪酬判断间的矛盾，暗示需进一步优化生成逻辑以实现真正公平。
原文摘要 · Abstract (English)
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.
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