  Large language models (LLMs) are increasingly being introduced in workplace
settings, with the goals of improving efficiency and fairness. However,
concerns have arisen regarding these models' potential to reflect or exacerbate
social biases and stereotypes. This study explores the potential impact of LLMs
on hiring practices. To do so, we conduct an algorithm audit of race and gender
biases in one commonly-used LLM, OpenAI's GPT-3.5, taking inspiration from the
history of traditional offline resume audits. We conduct two studies using
names with varied race and gender connotations: resume assessment (Study 1) and
resume generation (Study 2). In Study 1, we ask GPT to score resumes with 32
different names (4 names for each combination of the 2 gender and 4 racial
groups) and two anonymous options across 10 occupations and 3 evaluation tasks
(overall rating, willingness to interview, and hireability). We find that the
model reflects some biases based on stereotypes. In Study 2, we prompt GPT to
create resumes (10 for each name) for fictitious job candidates. When
generating resumes, GPT reveals underlying biases; women's resumes had
occupations with less experience, while Asian and Hispanic resumes had
immigrant markers, such as non-native English and non-U.S. education and work
experiences. Our findings contribute to a growing body of literature on LLM
biases, in particular when used in workplace contexts.
