Unraveling the hidden environmental impacts of AI solutions for environment

By Anne-Laure Ligozat, J. Lefèvre, Aurélie Bugeau, Jacques Combaz. 2021

196 citations recorded by the source corpus.

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.

Subfields: Computer Science Applications, Electrical and Electronic Engineering, Media Technology, AI Ethics, Fairness & Society

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Citations in the corpus

Measuring the Carbon Intensity of AI in Cloud Instances (2022, background): “Other studies have gone beyond simply tracking the emissions from training models, aiming to quantify the emissions resulting from manufacturing computing hardware [15], the broader impacts of sustainable AI [49], and the methodologies used to assess those impacts [21, 26].”
Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model (2022, background): “, 2021) as well as the life cycle analysis of the entire ML development and deployment cycle (Ligozat et al., 2021) and the certification of ML systems according to their social and environmental impacts (Gupta et al.”
DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining (2023, background): “Large language models are We hope to improve training efﬁciency and reduce the environmental impact of training large LMs [50; 28; 37; 29].”
Data Selection for Language Models via Importance Resampling (2023, background): “These benefits could reduce the environmental impact of LMs [77; 43; 46; 60] and reduce their biases and risks [9; 1; 22; 8].”
Large Language Model Alignment: A Survey (2023, background): “This has led to concerns on the carbon footprint of language models and their impact on climate change (Van Wynsberghe, 2021; Ligozat et al., 2021).”