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

International audienceIn the past ten years, artificial intelligence has encountered such dramatic progress thatit is now seen as a tool of choice to solve environmental issues and, in the first place, greenhousegas emissions (GHG). At the same time, the deep learning community began to realize that trainingmodels 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 theenvironment (AI for Green) and not only GHG, has never been addressed directly. In this article, wepropose to study the possible negative impacts of AI for Green. First, we review the different types ofAI impacts; then, we present the different methodologies used to assess those impacts and show howto apply life cycle assessment to AI services. Finally, we discuss how to assess the environmentalusefulness of a general AI service and point out the limitations of existing work in AI for Green