提出按环境公平原则分配AI负载，缓解地区间碳排放不公问题。
Towards Environmentally Equitable AI
- 设计基于环境公平的地理负载均衡算法，优化跨区域算力分配。
- 证明该方法可显著降低不同地区间的环境影响差异，提升系统公平性。
- 适合关注绿色AI、算力公平与可持续发展的研究者与工程师。
人工智能（AI）的迅猛发展带来了对全球部署的高能耗服务器的巨大需求，使AI系统的环境足迹日益受到关注。更关键的是，当前利用AI工作负载灵活性及系统管理方式，可能导致不同地区间产生截然不同的环境影响，加剧环境不公平问题，并引发意外的社会技术后果。本文主张将环境公平作为未来AI系统管理的核心优先事项，拓展了现有可持续AI资源管理的边界，也为AI公平性增添了新维度。具体而言，我们揭示了环境感知地理负载均衡在公平分摊环境成本方面的潜力，并探讨了相关的算法挑战。最后，讨论了若干未来方向，以充分发挥系统管理手段在缓解AI环境不公方面的潜力。
原文摘要 · Abstract (English)
The skyrocketing demand for artificial intelligence (AI) has created an enormous appetite for globally deployed power-hungry servers. As a result, the environmental footprint of AI systems has come under increasing scrutiny. More crucially, the current way that we exploit AI workloads' flexibility and manage AI systems can lead to wildly different environmental impacts across locations, increasingly raising environmental inequity concerns and creating unintended sociotechnical consequences. In this paper, we advocate environmental equity as a priority for the management of future AI systems, advancing the boundaries of existing resource management for sustainable AI and also adding a unique dimension to AI fairness. Concretely, we uncover the potential of equity-aware geographical load balancing to fairly re-distribute the environmental cost across different regions, followed by algorithmic challenges. We conclude by discussing a few future directions to exploit the full potential of system management approaches to mitigate AI's environmental inequity.
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