Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem

The position paper advocates for the development of an AI-augmented peer-review ecosystem in the machine learning community to address the increasing strain from manuscript submissions outpacing reviewer capacity. It proposes leveraging Large Language Models (LLMs) to assist in tasks such as factual verification, reviewer performance guidance, and decision-making support for Area Chairs, emphasizing the need for access to structured peer review data to facilitate this integration. This initiative is critical for maintaining the integrity and scalability of scientific validation in ML, addressing both technical and ethical challenges in the process.