  Large language models offer significant potential for increasing labour
productivity, such as streamlining personnel selection, but raise concerns
about perpetuating systemic biases embedded into their pre-training data. This
study explores the potential ethnic and gender bias of ChatGPT, a chatbot
producing human-like responses to language tasks, in assessing job applicants.
Using the correspondence audit approach from the social sciences, I simulated a
CV screening task with 34,560 vacancy-CV combinations where the chatbot had to
rate fictitious applicant profiles. Comparing ChatGPT's ratings of Arab, Asian,
Black American, Central African, Dutch, Eastern European, Hispanic, Turkish,
and White American male and female applicants, I show that ethnic and gender
identity influence the chatbot's evaluations. Ethnic discrimination is more
pronounced than gender discrimination and mainly occurs in jobs with favourable
labour conditions or requiring greater language proficiency. In contrast,
gender discrimination emerges in gender-atypical roles. These findings suggest
that ChatGPT's discriminatory output reflects a statistical mechanism echoing
societal stereotypes. Policymakers and developers should address systemic bias
in language model-driven applications to ensure equitable treatment across
demographic groups. Practitioners should practice caution, given the adverse
impact these tools can (re)produce, especially in selection decisions involving
humans.
