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

arXiv: https://arxiv.org/abs/2509.13400 · HTML: https://mlsanity.com/paper/2509.13400/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Can large language models provide useful feedback on research papers? A large-scale empirical analysis](https://mlsanity.com/paper/2310.01783/) (2023)
- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process](https://mlsanity.com/paper/2503.08569/) (2025)
- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates](https://mlsanity.com/paper/2405.02150/) (2024)
- [Large language models for automated scholarly paper review: A survey](https://mlsanity.com/paper/2501.10326/) (2025)
- [Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review](https://mlsanity.com/paper/2412.01708/) (2024)
- [Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment](https://mlsanity.com/paper/2306.03262/) (2023)
- [Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews](https://mlsanity.com/paper/2502.17086/) (2025)
- [From Replication to Redesign: Exploring Pairwise Comparisons for LLM-Based Peer Review](https://mlsanity.com/paper/2506.11343/) (2025)
- [Certifying Counterfactual Bias in LLMs](https://mlsanity.com/paper/2405.18780/) (2024)
- [A Theory of Response Sampling in LLMs: Part Descriptive and Part Prescriptive](https://mlsanity.com/paper/2402.11005/) (2024)

## Built on by (most cited first, 11 of 11)

- [Paraphrasing Adversarial Attack on LLM-as-a-Reviewer](https://mlsanity.com/paper/2601.06884/) (2026)
- [Gen-Review: A Large-scale Dataset of AI-Generated (and Human-written) Peer Reviews](https://mlsanity.com/paper/2510.21192/) (2025)
- [PeerPrism: Peer Evaluation Expertise vs Review-writing AI](https://mlsanity.com/paper/2604.14513/) (2026)
- [Inspectable AI for Science: A Research Object Approach to Generative AI Governance](https://mlsanity.com/paper/2604.11261/) (2026)
- [No Hidden Prompts Needed! You Can Game AI Peer Review with Presentation-Only Revisions](https://mlsanity.com/paper/2606.13044/) (2026)
- [The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing](https://mlsanity.com/paper/2609.07713/) (2026)
- [Are we still able to recognize pearls? Machine-driven peer review and the risk to creativity: An explainable RAG-XAI detection framework with markers extraction](https://mlsanity.com/paper/2604.07964/) (2026)
- [Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detection](https://mlsanity.com/paper/2606.30587/) (2026)
- [Need or Reputation? Region, National Income, and Academic Status in LLM Gatekeeping Decisions](https://mlsanity.com/paper/2608.05178/) (2026)
- [Who Pays for Open Review? Visible Author Reputation and Its Effect on Ratings](https://mlsanity.com/paper/2609.11983/) (2026)
- [A Missing Piece for Trustworthy AI Reviewers: From Benchmarking Rhetorical Robustness to SciCore Review](https://mlsanity.com/paper/2609.39027/) (2026)

## Abstract (arXiv)

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