Unraveling the hidden environmental impacts of AI solutions for environment

• URL: http://arxiv.org/abs/2110.11822v1
• Date: Fri, 22 Oct 2021 14:56:47 GMT
• Title: Unraveling the hidden environmental impacts of AI solutions for environment
• Authors: Anne-Laure Ligozat, Julien Lef\`evre, Aur\'elie Bugeau, Jacques Combaz
• Abstract summary: In the past ten years artificial intelligence has encountered such dramatic progress that it is seen now as a tool of choice to solve environmental issues. The deep learning community began to realize that training models with more and more parameters required a lot of energy and as a consequence GHG emissions. This article proposes to study the possible negative impact of "AI for green"
• Score: 0.04588028371034406
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the past ten years artificial intelligence has encountered such dramatic progress that it is seen now 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 required a lot of energy and as a consequence GHG emissions. To our knowledge, questioning the complete environmental impacts of AI methods for environment ("AI for green"), and not only GHG, has never been addressed directly. In this article we propose to study the possible negative impact of "AI for green" 1) by reviewing first the different types of AI impacts 2) by presenting the different methodologies used to assess those impacts, in particular life cycle assessment and 3) by discussing how to assess the environmental usefulness of a general AI service.

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