Article

A Framework for Environmentally Friendly AI

Posted November 10, 2021 in Business Technology & Digital Transformation Strategies, Data Analytics & Digital Technologies Cutter Business Technology Journal
EFAI
Rohit Nishant and Thompson S.H. Teo explore the environmental impact of AI and ML. Specifically, the applications where these technologies add the most value are those that require heterogenous data in complex settings (e.g., optimizing smart cities, modeling climate change). In the process of creating value, these large AI and ML models require energy-intensive computing, leaving a huge carbon foot­print. To counteract these concerns, Nishant and Teo offer the “Align, Reduce, Measure” (ARM) framework for mitigating the environmental impact of AI and ML algorithms. The framework encompasses the organiz­ational structure, addresses data heterogeneity, and measures results to create accountability.
About The Author
Rohit Nishant
Rohit Nishant is Associate Professor in the Department of Management Information Systems at Université Laval, Canada. His research has been published/accepted in several international journals, including MIS Quarterly, MIS Quarterly Executive, Journal of the Association for Information Systems, Journal of Strategic Information Systems, Information Systems Journal, Decision Sciences, and IEEE Transactions on Engineering Management. Dr. Nishant… Read More
Thompson S.H. Teo
Thompson S.H. Teo is Professor in the Department of Analytics and Operations at National University of Singapore Business School. His research interests include the adoption of IT, strategic IT planning, electronic government, knowledge management, and sustainability. Dr. Teo has published more than 250 papers in international journals and conferences. He has served as Senior Associate Editor for European Journal of Information Systems, Regional… Read More
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