Han, Y. et al.
arXiv:2412.06288 [cs.CY]
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.
The research is linked above.
This research paper quantifies the previously overlooked public health consequences of artificial intelligence (AI), focusing on the air pollution generated throughout its lifecycle—from chip manufacturing to data center operation. The authors present a methodology for modeling pollutant emissions and their resulting health impacts, finding that AI's environmental footprint translates to substantial health costs, potentially exceeding $20 billion annually in the US by 2030 and disproportionately affecting low-income communities. This "hidden toll" of AI, the paper argues, necessitates standardized reporting protocols for air pollutants and health impacts, the development of "health-informed AI" to mitigate adverse effects, and a focus on achieving public health equity.
Psychologists could find the information in the sources valuable as it highlights the potential mental health consequences of socioeconomic disparities exacerbated by AI's environmental impact. The sources reveal that the health burden of AI, particularly from data centers, is unevenly distributed and disproportionately affects low-income communities. This raises concerns about increased stress, anxiety, and depression in these communities due to factors like higher exposure to air pollution, reduced access to healthcare, and financial strain from increased health costs. Understanding these psychological impacts could inform interventions and policies aimed at mitigating the negative mental health consequences of AI's growth, particularly for vulnerable populations.
