# Position: The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process

arXiv: https://arxiv.org/abs/2502.00874 · HTML: https://mlsanity.com/paper/2502.00874/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 2 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment](https://mlsanity.com/paper/2109.09774/) (2021)
- [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)
- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process](https://mlsanity.com/paper/2010.05137/) (2020)
- [Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach](https://mlsanity.com/paper/2211.06398/) (2022)
- [Judgment Sieve: Reducing Uncertainty in Group Judgments through Interventions Targeting Ambiguity versus Disagreement](https://mlsanity.com/paper/2305.01615/) (2023)
- [What Have We Learned from OpenReview?](https://mlsanity.com/paper/2103.05885/) (2021)
- [Position: AI/ML Influencers Have a Place in the Academic Process](https://mlsanity.com/paper/2401.13782/) (2024)
- [Is Peer-Reviewing Worth the Effort?](https://mlsanity.com/paper/2412.14351/) (2024)

## Built on by (most cited first, 2 of 2)

- [Paper Copilot: Tracking the Evolution of Peer Review in AI Conferences](https://mlsanity.com/paper/2510.13201/) (2025)
- [OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models](https://mlsanity.com/paper/2505.21537/) (2025)

## Abstract (arXiv)

The rapid growth of submissions to top-tier Artificial Intelligence (AI) and Machine Learning (ML) conferences has prompted many venues to transition from closed to open review platforms. Some have fully embraced open peer reviews, allowing public visibility throughout the process, while others adopt hybrid approaches, such as releasing reviews only after final decisions or keeping reviews private despite using open peer review systems. In this work, we analyze the strengths and limitations of these models, highlighting the growing community interest in transparent peer review. To support this discussion, we examine insights from Paper Copilot, a website launched two years ago to aggregate and analyze AI / ML conference data while engaging a global audience. The site has attracted over 200,000 early-career researchers, particularly those aged 18-34 from 177 countries, many of whom are actively engaged in the peer review process. Drawing on our findings, this position paper advocates for a more transparent, open, and well-regulated peer review aiming to foster greater community involvement and propel advancements in the field.