The Unequal Burden of Environmental and Health Costs of Energy-Intensive Artificial Intelligence.

We examined environmental and public health implications of energy-intensive artificial intelligence (AI) infrastructure, emphasizing health equity, workers, and vulnerable communities. We analyzed evidence on AI infrastructure across its life cycle, including data-center electricity demand, fossil-fuel power, water use, heat, noise, critical-mineral extraction, occupational exposures, and electronic waste (e-waste). AI infrastructure can expose nearby communities and workers to air pollution, heat, noise, water stress, hazardous mining conditions, and e-waste toxicants. In 2023, US data centers consumed 4.4% of national electricity, much from fossil fuel combustion, contributing to climate change and air pollution. Data centers also consume large water volumes and generate e-waste, while demand for high-performance hardware intensifies upstream environmental and occupational hazards. These risks fall disproportionately on low-income, Indigenous, and other marginalized communities near data centers, mines, power plants, and e-waste recycling sites. Responsible AI governance should treat energy-intensive AI as a public health and health equity issue requiring health impact assessments, exposure surveillance, equitable siting, accountability, community participation, and fair distribution of AI's health-related benefits and burdens. (Am J Public Health. 2026;116(10):1529-1538. https://doi.org/10.2105/AJPH.2026.308683).