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

Created by
  • Haebom

Author

Nuo Chen, Moming Duan, Andre Huikai Lin, Qian Wang, Jiaying Wu, Bingsheng He

Outline

This paper analyzes the structural challenges arising from the rapid expansion of artificial intelligence (AI) conferences, using data-driven approaches, and proposes a new model for sustainable, inclusive, and resilient AI research. It identifies four key challenges: excessive publication volume, environmental impact, negative online discourse and mental health issues, and overcrowding. To address these challenges, it proposes the Community Federation Conference (CFC) model, which separates peer review, publishing, and networking into globally coordinated yet locally operated components.

Takeaways, Limitations

Takeaways:
Clearly presenting the structural problems of AI societies based on data.
Proposing a new model (CFC) for building a sustainable and inclusive AI research ecosystem.
Raising awareness of the negative impacts (environmental, mental health, etc.) of excessive growth in AI societies.
Limitations:
Lack of detailed discussion on the specific operational mechanisms and feasibility of the CFC model.
The scope and representativeness of the presented data need to be reviewed.
Lack of analysis of the long-term effects and anticipated problems of the CFC model.
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