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.

社会科学研究表明，具有表明特定种族或性别的姓名的候选人在就业实践中经常面临歧视。 similarly, 大型语言模型 (LLMs) 在各种应用中展示了种族和性别偏见。在本研究中，我们利用 GPT-3.5-Turbo 和 Llama 3-70B-Instruct 来模拟对具有 320 个姓氏强烈表示其种族和性别的候选人的招聘决策和薪水建议，覆盖了超过 750,000 个提示。我们的实证结果表明，这些模型偏好招聘白人女性名字听起来的候选人，并选择其他人口群体中的 40 个职业。此外，即使在具有相同资格的候选人中，薪水建议在不同子群体之间也会有高达 5% 的差异。与现实劳动力数据的比较显示，与美国劳动力市场特征不一致，凸显了对由 LLMs 驱动的系统的风险调查的必要性。

你得是医生，林”：对大型语言模型在就业推荐中基于姓名的偏见的研究

"You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of  Large Language Models in Employment Recommendations

We examine whether large language models (LLMs) exhibit race- and
gender-based name discrimination in hiring decisions, similar to classic
findings in the social sciences (Bertrand and Mullainathan, 2004). We design a
series of templatic prompts to LLMs to write an email to a named job applicant
informing them of a hiring decision. By manipulating the applicant's first
name, we measure the effect of perceived race, ethnicity, and gender on the
probability that the LLM generates an acceptance or rejection email. We find
that the hiring decisions of LLMs in many settings are more likely to favor
White applicants over Hispanic applicants. In aggregate, the groups with the
highest and lowest acceptance rates respectively are masculine White names and
masculine Hispanic names. However, the comparative acceptance rates by group
vary under different templatic settings, suggesting that LLMs' race- and
gender-sensitivity may be idiosyncratic and prompt-sensitive.

我们研究大型语言模型（LLMs）是否在招聘决策中表现出基于种族和性别的姓名歧视，类似社会科学中的经典研究结果（Bertrand 和 Mullainathan，2004 年）。通过为 LLMs 设计一系列模板提示，要求其给被命名的求职者写一封邮件，告知其招聘决定，我们通过操纵求职者的名字，衡量 LLLMS 生成接受或拒绝邮件的概率如何受到被认知的种族、民族和性别的影响。我们发现在许多情况下，LLLMS 的招聘决策更有可能倾向于白人申请者而不是西班牙裔申请者。总体而言，接受率最高和最低的群体分别是男性白人姓名和男性西班牙裔姓名。然而，在不同的模板设置下，各群体的接受率有所不同，这表明 LLLMS 对种族和性别的敏感性可能是独特的，并且受到模板提示的影响。