# Who Pays for Open Review? Visible Author Reputation and Its Effect on Ratings

arXiv: https://arxiv.org/abs/2609.11983 · HTML: https://mlsanity.com/paper/2609.11983/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 7 Sep 2026.

## Builds on (most cited first, 7 of 7)

- [The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates](https://mlsanity.com/paper/2405.02150/) (2024)
- [An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process](https://mlsanity.com/paper/2010.05137/) (2020)
- [Uncovering Latent Biases in Text: Method and Application to Peer Review](https://mlsanity.com/paper/2010.15300/) (2020)
- [Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews](https://mlsanity.com/paper/2509.13400/) (2025)
- [Does double-blind peer-review reduce bias? Evidence from a top computer science conference](https://mlsanity.com/paper/2101.02701/) (2021)
- [Cite-seeing and Reviewing: A Study on Citation Bias in Peer Review](https://mlsanity.com/paper/2203.17239/) (2022)
- [Estimating the Causal Effect of Early ArXiving on Paper Acceptance](https://mlsanity.com/paper/2306.13891/) (2023)

## Abstract (arXiv)

An OpenReview bug in November 2025 broke anonymity at several conferences and prompted calls for open review, which motivate us to ask what shifting from blind to open would mean for authors. Analyzing over 18,000 reviewed submissions to ICLR 2026, split into de facto open and blind groups by arXiv preprint timing, we find that ratings rise with author reputation under both mechanisms, with a steeper slope under open review that is statistically significant, and that the open-blind difference is concentrated at the borderline ratings. The pattern holds across five reputation proxies (including institution, h-index, and citation count), three author-aggregation rules, and five definitions of the open window. A controlled simulation with five AI models as reviewers, holding the manuscript fixed and varying the author reputation, reproduces the effect. With claude-opus-5 as the reviewer, for example, rating rises by 0.5 points as the author moves from low to high reputation.