Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews

ACL ARR 2025 July Submission1090 Authors

Abstract: The adoption of large language models (LLMs) is transforming the peer review process, from assisting reviewers in writing more detailed evaluations to generating entire reviews automatically. While these capabilities offer exciting opportunities, they also raise critical concerns about fairness and reliability. In this paper, we investigate bias in LLM-generated peer reviews by conducting controlled experiments on sensitive metadata, including author affiliation and gender. Our analysis consistently shows affiliation bias favoring institutions highly ranked on common academic rankings. Additionally, we find some gender preferences, which, even though subtle in magnitude, have the potential to compound over time. Notably, we uncover implicit biases that become more evident with token-based soft ratings.

Paper Type: Short

Research Area: Ethics, Bias, and Fairness

Research Area Keywords: Fairness

Contribution Types: Model analysis & interpretability

Languages Studied: English

Reassignment Request Area Chair: This is not a resubmission

Reassignment Request Reviewers: This is not a resubmission

A1 Limitations Section: This paper has a limitations section.

A2 Potential Risks: Yes

A2 Elaboration: Limitations and Ethics sections.

B Use Or Create Scientific Artifacts: Yes

B1 Cite Creators Of Artifacts: Yes

B1 Elaboration: References section

B2 Discuss The License For Artifacts: Yes

B2 Elaboration: Appendix C and J

B3 Artifact Use Consistent With Intended Use: Yes

B3 Elaboration: Appendix J

B4 Data Contains Personally Identifying Info Or Offensive Content: Yes

B4 Elaboration: Appendices D, E

B5 Documentation Of Artifacts: Yes

B5 Elaboration: Section 2.3, Appendix B, D, E

B6 Statistics For Data: Yes

B6 Elaboration: Section 2.3

C Computational Experiments: Yes

C1 Model Size And Budget: Yes

C1 Elaboration: Appendix C.

C2 Experimental Setup And Hyperparameters: Yes

C2 Elaboration: Section 2 and Appendix B, D, E

C3 Descriptive Statistics: Yes

C3 Elaboration: Section 3

C4 Parameters For Packages: Yes

C4 Elaboration: Section 3 and Appendix C.

D Human Subjects Including Annotators: No

D1 Instructions Given To Participants: N/A

D2 Recruitment And Payment: N/A

D3 Data Consent: N/A

D4 Ethics Review Board Approval: N/A

D5 Characteristics Of Annotators: N/A

E Ai Assistants In Research Or Writing: No

E1 Information About Use Of Ai Assistants: N/A

Author Submission Checklist: yes

Submission Number: 1090