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Strengthening Subcommunities: Towards Sustainable Growth in AI Research

Artificial Intelligence 2022-04-19 v1 Machine Learning

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.

Keywords

Cite

@article{arxiv.2204.08377,
  title  = {Strengthening Subcommunities: Towards Sustainable Growth in AI Research},
  author = {Andi Peng and Jessica Zosa Forde and Yonadav Shavit and Jonathan Frankle},
  journal= {arXiv preprint arXiv:2204.08377},
  year   = {2022}
}

Comments

ICLR 2022 ML Evaluation Standards Workshop