We investigate whether LLMs display a well-known human cognitive bias, the attraction effect, in hiring decisions. The attraction effect occurs when the presence of an inferior candidate makes a superior candidate more appealing, increasing the likelihood of the superior candidate being chosen over a non-dominated competitor. Our study finds consistent and significant evidence of the attraction effect in GPT-3.5 and GPT-4 when they assume the role of a recruiter. Irrelevant attributes of the decoy, such as its gender, further amplify the observed bias. GPT-4 exhibits greater bias variation than GPT-3.5. Our findings remain robust even when warnings against the decoy effect are included and the recruiter role definition is varied.
Cite
@article{arxiv.2409.15299,
title = {Irrelevant Alternatives Bias Large Language Model Hiring Decisions},
author = {Kremena Valkanova and Pencho Yordanov},
journal= {arXiv preprint arXiv:2409.15299},
year = {2024}
}