Strengthening Subcommunities: Towards Sustainable Growth in AI Research

TL;DR

This paper proposes decentralizing AI research peer review to strengthen subcommunities, improve relevance, and support sustainable growth by leveraging successful existing models within AI.

Contribution

It introduces a decentralized review model tailored to subcommunities, aiming to address peer review challenges and promote sustainable development in AI research.

Findings

Decentralized review models have succeeded in some AI subcommunities.

The proposed approach encourages community-specific publication practices.

This model can help manage AI's rapid growth and diversity.

Abstract

AI's rapid growth has been felt acutely by scholarly venues, leading to growing pains within the peer review process. These challenges largely center on the inability of specific subareas to identify and evaluate work that is appropriate according to criteria relevant to each subcommunity as determined by stakeholders of that subarea. We set forth a proposal that re-focuses efforts within these subcommunities through a decentralization of the reviewing and publication process. Through this re-centering effort, we hope to encourage each subarea to confront the issues specific to their process of academic publication and incentivization. This model has historically been successful for several subcommunities in AI, and we highlight those instances as examples for how the broader field can continue to evolve despite its continually growing size.