Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908996345004032 |
|---|---|
| author | Vasu, Sai Suresh Macharla Sheth, Ivaxi Wang, Hui-Po Binkyte, Ruta Fritz, Mario |
| author_facet | Vasu, Sai Suresh Macharla Sheth, Ivaxi Wang, Hui-Po Binkyte, Ruta Fritz, Mario |
| contents | The adoption of large language models (LLMs) is transforming the peer review process, from assisting reviewers in writing detailed evaluations to generating entire reviews automatically. While these capabilities offer new opportunities, they also raise concerns about fairness and reliability. In this paper, we investigate bias in LLM-generated peer reviews through controlled interventions on author metadata, including affiliation, gender, seniority, and publication history. Our analysis consistently shows a strong affiliation bias favoring authors from highly ranked institutions. We also identify directional preferences associated with seniority and prior publication record, which can influence acceptance decisions for borderline papers. Gender effects are smaller but present in several models. Notably, implicit biases become more pronounced when examining token-level soft ratings, suggesting that alignment may mask but not fully eliminate underlying preferences |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13400 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews Vasu, Sai Suresh Macharla Sheth, Ivaxi Wang, Hui-Po Binkyte, Ruta Fritz, Mario Computers and Society Artificial Intelligence The adoption of large language models (LLMs) is transforming the peer review process, from assisting reviewers in writing detailed evaluations to generating entire reviews automatically. While these capabilities offer new opportunities, they also raise concerns about fairness and reliability. In this paper, we investigate bias in LLM-generated peer reviews through controlled interventions on author metadata, including affiliation, gender, seniority, and publication history. Our analysis consistently shows a strong affiliation bias favoring authors from highly ranked institutions. We also identify directional preferences associated with seniority and prior publication record, which can influence acceptance decisions for borderline papers. Gender effects are smaller but present in several models. Notably, implicit biases become more pronounced when examining token-level soft ratings, suggesting that alignment may mask but not fully eliminate underlying preferences |
| title | Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2509.13400 |