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。