Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference
🤖 AI Summary
The centralized conference model dominating top-tier AI venues is triggering four interrelated systemic crises: scientific (average annual publication exceeding 4.5 papers per researcher), environmental (carbon emissions per conference surpassing the host city’s daily average), psychological (71% of online discussions exhibiting negative sentiment), and logistical (chronic venue overcapacity). This paper presents the first empirical, multidimensional analysis substantiating this unsustainability. We propose the “Community-Federated Conference” (CFC) paradigm—a novel framework that decouples peer review, presentation, and networking functions across globally distributed local nodes, enabling decentralized academic workflows. CFC preserves rigorous scholarly standards while substantially reducing carbon footprint, alleviating publication pressure, enhancing participation inclusivity, and improving community well-being. The framework provides both a theoretically grounded and practically implementable pathway toward a decentralized, low-carbon, and sustainable research collaboration ecosystem for AI.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Centralized AI conferences face unsustainable growth and structural crisis.
Rising per-author publication rates and environmental impact strain resources.
Negative sentiment and mental health concerns plague the AI community.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Proposes Community-Federated Conference model
Decentralizes peer review and networking
Reduces carbon footprint and increases inclusivity
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