As artificial intelligence continues to transform society, researchers are sounding the alarm about its potential negative impacts on the environment and communities. Mar Hicks, associate professor of data science at the University of Virginia, and Jess Reia, assistant professor of data science, have been awarded a two-year, $300,000 grant from the National Science Foundation to investigate these critical issues. Working with the Data and Society Research Institute, they will examine the effects of large data centers built by AI companies on local environments and communities.
The team will engage directly with affected communities to develop frameworks for assessing AI’s impact on the environment and society, using participatory methods such as interviews, workshops, and pilot studies. This project is part of NSF’s Responsible Design and Deployment of Technologies program, which has provided over $18 million in support to 44 research teams nationwide.
Examining the Social and Environmental Impacts of Artificial Intelligence
As artificial intelligence (AI) continues to expand its presence across various sectors, researchers are raising concerns about the potential negative consequences of this technological transformation. The National Science Foundation (NSF) has recently awarded a two-year, $300,000 grant to a project led by Associate Professor Mar Hicks and Assistant Professor Jess Reia of the University of Virginia’s School of Data Science, in partnership with Tamara Kneese and the Data and Society Research Institute. This project aims to investigate the social and environmental impacts of large data centers built by AI companies on local communities and the physical environment.
The research team will focus on the often-overlooked aspects of AI infrastructure, including electronic waste, land use, energy consumption, and water usage. While some AI developers are assessing the carbon costs of their infrastructure, these other concerns have received relatively little attention. By working directly with affected communities, the project team seeks to ensure that their voices are heard and accounted for by governments as they formulate AI regulations and policies.
Using participatory methods, the project team will conduct interviews with stakeholders, organize workshops, and pilot studies to develop frameworks for assessing AI’s impact on the environment and society. These frameworks will be based on the experiences and knowledge of those most affected by AI infrastructure. This approach is crucial in ensuring that the concerns and needs of local communities are taken into account when designing and implementing AI systems.
The NSF grant is part of the Responsible Design and Deployment of Technologies program, an initiative providing over $18 million in support to 44 multidisciplinary, multi-sector research teams across the country. This project builds on Hicks’ and Reia’s previous work and research, including a conference co-organized by Hicks this summer, which brought together experts from various fields to share their research on lessons from history that can guide current approaches to dealing with AI’s social and environmental impacts.
The Importance of Community Engagement in AI Research
The project’s focus on community engagement is critical in ensuring that the concerns and needs of local communities are taken into account when designing and implementing AI systems. By working directly with affected communities, the research team can gather valuable insights into the social and environmental impacts of AI infrastructure. This approach also enables the team to develop frameworks for assessing AI’s impact that are based on the experiences and knowledge of those most affected.
Community engagement is essential in addressing the often-overlooked aspects of AI infrastructure, such as electronic waste, land use, energy consumption, and water usage. These concerns have significant implications for local communities, including environmental degradation, health risks, and social inequality. By involving local communities in the research process, the project team can identify potential solutions that are tailored to their specific needs and contexts.
The project’s participatory approach is also crucial in ensuring that the voices of affected communities are heard and accounted for by governments as they formulate AI regulations and policies. This is particularly important in addressing the social and environmental impacts of AI infrastructure, which often disproportionately affect marginalized or vulnerable populations.
The Role of Historical Lessons in Informing AI Research
The project’s focus on historical lessons is also significant in informing current approaches to dealing with AI’s social and environmental impacts. Hicks’ conference this summer brought together experts from various fields to share their research on lessons from history that can guide our current approaches to addressing the negative consequences of technological transformations.
Historical lessons can provide valuable insights into the potential risks and unintended consequences of AI infrastructure. For example, the development of industrial-scale data centers has parallels with the growth of industrial agriculture or manufacturing, which have had significant environmental and social impacts. By examining these historical precedents, researchers can identify potential solutions that can mitigate the negative consequences of AI infrastructure.
The project’s focus on historical lessons also highlights the importance of interdisciplinary research in addressing the complex challenges posed by AI. By bringing together experts from various fields, including history, sociology, environmental science, and computer science, the project team can develop a more comprehensive understanding of AI’s social and environmental impacts.
The Broader Implications of AI Infrastructure for Society and Environment
The project’s findings have significant implications for society and the environment beyond the local communities affected by AI infrastructure. The growth of industrial-scale data centers has far-reaching consequences for energy consumption, electronic waste, land use, and water usage, which can contribute to climate change, environmental degradation, and social inequality.
Moreover, the project’s focus on community engagement and historical lessons highlights the need for more inclusive and sustainable approaches to designing and implementing AI systems. This requires a shift in perspective from solely focusing on the technical capabilities of AI to considering its broader social and environmental implications.
The project’s findings also have significant policy implications, highlighting the need for governments to formulate regulations and policies that take into account the concerns and needs of local communities affected by AI infrastructure. This requires a more nuanced understanding of AI’s social and environmental impacts, as well as a commitment to developing more sustainable and equitable approaches to technological development.
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