Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions

• Published in: Sustainability Vol. 14; no. 9; p. 5172
• Main Authors: Ligozat, Anne-Laure, Lefevre, Julien, Bugeau, Aurélie, Combaz, Jacques
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25.04.2022; MDPI
• Subjects: Algorithms; Artificial Intelligence; Carbon footprint; Computer Science; Deep learning; Emissions; Energy consumption; Energy efficiency; Environment and Society; Environmental impact; Environmental Sciences; Greenhouse effect; Greenhouse gases; Machine learning; Sustainability; carbon footprint; LCA; sustainability; artificial intelligence
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su14095172

In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and, in the first place, greenhouse gas emissions (GHG). At the same time, the deep learning community began to realize that training models with more and more parameters require a lot of energy and, as a consequence, GHG emissions. To our knowledge, questioning the complete net environmental impacts of AI solutions for the environment (AI for Green) and not only GHG, has never been addressed directly. In this article, we propose to study the possible negative impacts of AI for Green. First, we review the different types of AI impacts; then, we present the different methodologies used to assess those impacts and show how to apply life cycle assessment to AI services. Finally, we discuss how to assess the environmental usefulness of a general AI service and point out the limitations of existing work in AI for Green.