呼吁AI/ML领域推行更透明规范的同行评审制度。
Position: The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process
- 分析现有开放与混合评审模式的优劣，推动透明化改革。
- 基于20万+早期研究者使用数据，验证社区对透明评审的强烈需求。
- 适合关注学术公正、希望参与评审的青年学者参考。
顶级人工智能（AI）与机器学习（ML）会议投稿量激增，促使多个会议从封闭评审转向开放评审平台。部分会议全面采用公开评审，全程可见评审过程；另一些则采取混合模式，仅在最终决定后公布评审意见，或虽用开放系统但仍保持评审私密。本文分析了这些模式的优劣，强调学界对透明评审日益增长的兴趣。为支持这一讨论，我们基于两年来运行的Paper Copilot网站数据展开研究，该平台聚合并分析了AI/ML会议数据，已吸引超过20万早期研究者，主要为18-34岁、来自177个国家的青年学者，其中许多积极参与评审流程。基于上述发现，本文主张建立更透明、开放且受良好监管的同行评审机制，以促进社区广泛参与，推动领域持续发展。
原文摘要 · Abstract (English)
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.
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