Abstract
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.- Anthology ID:
- 2024.findings-emnlp.405
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6899–6912
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.405/
- DOI:
- 10.18653/v1/2024.findings-emnlp.405
- Cite (ACL):
- Kremena Valkanova and Pencho Yordanov. 2024. Irrelevant Alternatives Bias Large Language Model Hiring Decisions. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6899–6912, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Irrelevant Alternatives Bias Large Language Model Hiring Decisions (Valkanova & Yordanov, Findings 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.405.pdf