Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference
This addresses a foundational problem for the AI research community by highlighting systemic issues that threaten scientific dissemination and equity, though it is incremental in proposing an alternative model rather than a radical overhaul.
The paper tackles the unsustainable centralized model of AI conferences, identifying strains in publication rates, environmental impact, psychological well-being, and logistics, and proposes a Community-Federated Conference model as a solution.
Artificial Intelligence (AI) conferences are essential for advancing research, sharing knowledge, and fostering academic community. However, their rapid expansion has rendered the centralized conference model increasingly unsustainable. This paper offers a data-driven diagnosis of a structural crisis that threatens the foundational goals of scientific dissemination, equity, and community well-being. We identify four key areas of strain: (1) scientifically, with per-author publication rates more than doubling over the past decade to over 4.5 papers annually; (2) environmentally, with the carbon footprint of a single conference exceeding the daily emissions of its host city; (3) psychologically, with 71% of online community discourse reflecting negative sentiment and 35% referencing mental health concerns; and (4) logistically, with attendance at top conferences such as NeurIPS 2024 beginning to outpace venue capacity. These pressures point to a system that is misaligned with its core mission. In response, we propose the Community-Federated Conference (CFC) model, which separates peer review, presentation, and networking into globally coordinated but locally organized components, offering a more sustainable, inclusive, and resilient path forward for AI research.