The Unpaid Toll: Quantifying the Public Health Impact of AI

2024-12-09

arXiv.org

AI Summary

The environmental health impacts and associated environmental injustice of AI’s full lifecycle—from semiconductor manufacturing to datacenter operations—remain poorly quantified. Method: We develop the first integrated AI health impact quantification framework, combining multi-source emission inventories, life cycle assessment, atmospheric transport modeling, health risk assessment, and spatial environmental justice statistics. Contribution/Results: Training Llama3.1 generates PM₂.₅ emissions equivalent to over 10,000 round-trip automobile journeys between Los Angeles and New York. Health burdens exhibit pronounced spatial heterogeneity—up to a 200-fold disparity across U.S. census tracts—disproportionately affecting marginalized communities. By 2030, AI-related datacenter operations in the U.S. are projected to incur annual health costs exceeding $20 billion. We propose mandatory disclosure standards for AI health externalities and a health-centered governance framework to advance equitable, sustainable AI development.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: AI for Accessibility
• Machine Learning: Efficient ML / Green AI

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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 AI's growing environmental footprint, the public health burden, a hidden toll of AI, has been largely overlooked. Specifically, AI's lifecycle, from chip manufacturing to data center operation, significantly degrades air quality through emissions of criteria air pollutants such as fine particulate matter, substantially impacting public health. This paper introduces a methodology to model pollutant emissions across AI's lifecycle, quantifying the public health impacts. Our findings reveal that training an AI model of the Llama3.1 scale can produce air pollutants equivalent to more than 10,000 round trips by car between Los Angeles and New York City. The total public health burden of U.S. data centers in 2030 is valued at up to more than $20 billion per year, double that of U.S. coal-based steelmaking and comparable to that of on-road emissions of California. Further, the public health costs unevenly impact economically disadvantaged communities, where the per-household health burden could be 200x more than that in less-impacted communities. We recommend adopting a standard reporting protocol for criteria air pollutants and the public health costs of AI, paying attention to all impacted communities, and implementing health-informed AI to mitigate adverse effects while promoting public health equity.

Problem

• Quantifying data centers' lifecycle air pollutant emissions and public health impacts
• Addressing disproportionate health burdens on disadvantaged communities from AI expansion
• Developing health-informed computing framework to mitigate adverse health effects

Innovation

• Models lifecycle pollutant emissions of data centers
• Quantifies public health impacts of computing tasks
• Proposes health-informed framework for workload scheduling