Unraveling the Hidden Environmental Impacts of AI Solutions for Environment
This work highlights a critical gap in evaluating the net environmental effects of AI for Green, which is important for researchers and policymakers to avoid unintended harm.
The paper addresses the overlooked negative environmental impacts of AI solutions designed for environmental benefits, such as greenhouse gas emissions from training large models, and proposes a framework for assessing these impacts using life cycle assessment.
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 requires 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.