  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.
