Unraveling the Hidden Environmental Impacts of AI Solutions for Environment Life Cycle Assessment of AI Solutions
Abstract
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.
Origin : Publisher files allowed on an open archive
