The surging demand for AI has led to a rapid expansion of energy-intensive data centers, impacting the environment through escalating carbon emissions and water consumption. While significant attention has been paid to data centers' growing environmental footprint, the public health burden, a hidden toll of data centers, has been largely overlooked. Specifically, data centers' lifecycle, from chip manufacturing to operation, can significantly degrade air quality through emissions of criteria air pollutants such as fine particulate matter, substantially impacting public health. This paper introduces a principled methodology to model lifecycle pollutant emissions for data centers and computing tasks, quantifying the public health impacts. Our findings reveal that training a large AI model comparable to the Llama-3.1 scale can produce air pollutants equivalent to more than 10,000 round trips by car between Los Angeles and New York City. The growing demand for AI is projected to push the total annual public health burden of U.S. data centers up to more than $20 billion in 2028, rivaling that of on-road emissions of California. Further, the public health costs are more felt in disadvantaged communities, where the per-household health burden could be 200x more than that in less-impacted communities. Finally, we propose a health-informed computing framework that explicitly incorporates public health risk as a key metric for scheduling data center workloads across space and time, which can effectively mitigate adverse health impacts while advancing environmental sustainability. More broadly, we also recommend adopting a standard reporting protocol for the public health impacts of data centers and paying attention to all impacted communities.

翻译：人工智能需求的激增导致高能耗数据中心迅速扩张，通过不断增加的碳排放和用水量对环境造成影响。尽管数据中心的日益增长的环境足迹已受到广泛关注，但其对公共健康的负担——这一数据中心隐藏的代价——在很大程度上被忽视了。具体而言，数据中心的整个生命周期，从芯片制造到运营，会通过排放细颗粒物等标准空气污染物显著降低空气质量，从而对公共健康产生重大影响。本文提出了一种原则性方法，用于建模数据中心和计算任务的生命周期污染物排放，并量化其对公共健康的影响。我们的研究结果表明，训练一个与Llama-3.1规模相当的大型AI模型所产生的空气污染物，相当于超过10,000次洛杉矶与纽约市之间的汽车往返行程。预计人工智能需求的增长将推动美国数据中心的年度公共健康总负担在2028年超过200亿美元，可与加利福尼亚州的道路排放负担相匹敌。此外，公共健康成本在弱势社区感受更为明显，其每户家庭的健康负担可能比受影响较小社区高出200倍。最后，我们提出了一个健康感知的计算框架，该框架明确将公共健康风险作为一项关键指标，用于跨时空调度数据中心工作负载，这可以在推进环境可持续性的同时有效减轻不利的健康影响。更广泛地说，我们还建议采用一项标准报告协议来评估数据中心对公共健康的影响，并关注所有受影响的社区。