Strengthening Subcommunities: Towards Sustainable Growth in AI Research
This addresses peer review scalability issues for AI researchers and venues, though it builds on existing decentralized models rather than introducing a fundamentally new solution.
The paper tackles the problem of peer review challenges in rapidly growing AI research by proposing a decentralized reviewing and publication model that allows subcommunities to set their own evaluation criteria, highlighting historical successes of this approach in specific AI subareas.
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.