Strengthening Subcommunities: Towards Sustainable Growth in AI Research
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 2 of its 7 references that are in the map. First posted on arXiv 18 Apr 2022.
Builds on
2 of 7 references.
References · 2
Built on by
Citing papers per year: 2024: 1.
1 paper.
- 2024Group Fairness in Peer Reviewcited by 5
- 2024Group Fairness in Peer Review4 Oct 2024