“你得是个医生，林”：大语言模型在就业推荐中基于姓名的偏见研究
"You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of Large Language Models in Employment Recommendations
AI总结：
本研究针对大语言模型的就业推荐偏见问题，用两款主流LLM基于320个带种族性别信号的姓名、超75万条提示模拟招聘，发现模型存在白人女性姓名偏好且薪资推荐差异最高达5%，与美国劳动力市场特征不符，凸显LLM就业应用的风险调查必要性。
摘要：
社会科学研究表明，姓名带有特定种族或性别特征的求职者在就业过程中常遭受歧视。类似地，大语言模型（LLM）在各类应用中也被证实存在种族和性别偏见。本研究使用GPT-3.5-Turbo和Llama 3-70B-Instruct，基于320个带有强烈种族和性别信号的名字，通过超过75万条提示模拟招聘决策与薪资推荐。实证结果显示，在40种职业中，这些模型更倾向于聘用姓名听起来像白人女性的求职者，而非其他人口群体。此外，即使在资质完全相同的求职者中，不同子群体的薪资推荐差异最高可达5%。与真实劳动力数据的对比显示，模型输出与美国劳动力市场特征的一致性较差，凸显了对LLM驱动系统开展风险调查的必要性。
英文摘要：
Social science research has shown that candidates with names indicative of certain races or genders often face discrimination in employment practices. Similarly, Large Language Models (LLMs) have demonstrated racial and gender biases in various applications. In this study, we utilize GPT-3.5-Turbo and Llama 3-70B-Instruct to simulate hiring decisions and salary recommendations for candidates with 320 first names that strongly signal their race and gender, across over 750,000 prompts. Our empirical results indicate a preference among these models for hiring candidates with White female-sounding names over other demographic groups across 40 occupations. Additionally, even among candidates with identical qualifications, salary recommendations vary by as much as 5% between different subgroups. A comparison with real-world labor data reveals inconsistent alignment with U.S. labor market characteristics, underscoring the necessity of risk investigation of LLM-powered systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
