Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender?
This addresses a critical fairness issue in AI deployment for hiring, highlighting potential biases that could affect job applicants from marginalized groups, though it is incremental as it builds on existing social science research.
The study investigated whether large language models (LLMs) exhibit discrimination based on race, ethnicity, and gender in hiring decisions, finding that LLMs are more likely to favor White applicants over Hispanic applicants, with masculine White names having the highest acceptance rates and masculine Hispanic names the lowest.
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.