忽视AI可持续性正加剧全球技术军备竞赛
Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race
- 用马克思主义框架分析AI发展背后的物质条件
- 提出兼顾气候与资源的可持续AI框架
- 适合关注AI伦理与政策的研究者阅读
可持续性包含经济、环境和社会三方面，但当前关于可持续AI的讨论主要集中于环境层面，忽视了经济与社会维度。实现真正可持续的AI需调和环境可持续性（减少气候影响）与社会可持续性（公平获取资源）之间的矛盾。提升资源可及性虽推动普及，却常忽略其环境代价。本文主张协调气候意识与资源意识，否则将助长全球AI军备竞赛。基于卡尔·马克思的历史唯物主义基底-上层建筑框架，分析当前AI进展及其话语构建。进一步提出气候与资源感知机器学习（CARAML）框架，提供个人、社区、产业、政府及全球层面的可操作建议。
原文摘要 · Abstract (English)
Sustainability encompasses three key facets: economic, environmental, and social. However, the nascent discourse on sustainable artificial intelligence (AI) predominantly focuses on the environmental sustainability of AI, neglecting the economic and social aspects. Achieving truly sustainable AI necessitates addressing the tension between its environmental sustainability, which emphasises mitigating AI's climate impact, and its social sustainability, hinging on equitable access to AI development resources. This push for increased accessibility, however, often overlooks the environmental costs of expanding such resource usage. This position paper argues that reconciling climate awareness and resource awareness is essential to realising truly sustainable AI, and neglecting these factors fuels a global AI arms race. Applying Karl Marx's base-superstructure framework from historical materialism, we analyse how the material conditions are shaping the current AI progress and the discourse surrounding it. Further, we introduce the Climate and Resource Aware Machine Learning (CARAML) framework with actionable recommendations spanning individual, community, industry, government, and global levels to achieve sustainable AI.
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