立場：當前的人工智慧會議模式不可持續！診斷集中式AI會議的危機

摘要

人工智慧（AI）會議對於推動研究、分享知識及促進學術社群至關重要。然而，其快速擴張已使集中式會議模式日益難以持續。本文基於數據分析，揭示了一場威脅科學傳播、公平性及社群福祉基礎目標的結構性危機。我們識別出四大壓力領域：(1) 科學層面，每位作者的平均發表率在過去十年間翻倍，現已超過每年4.5篇論文；(2) 環境層面，單一會議的碳足跡已超過其舉辦城市的日排放量；(3) 心理層面，線上社群討論中71%反映負面情緒，35%提及心理健康問題；(4) 後勤層面，如NeurIPS 2024等頂級會議的參與人數開始超出場地容量。這些壓力表明現行系統與其核心使命存在偏差。對此，我們提出「社群聯邦會議」（Community-Federated Conference, CFC）模型，將同行評審、論文發表與網絡交流分離為全球協調但本地組織的模塊，為AI研究開闢一條更可持續、包容且具韌性的發展道路。

English

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.

立場：當前的人工智慧會議模式不可持續！診斷集中式AI會議的危機

Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference

摘要

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