Abstract

This essay responds to the call for exploring the wider societal risks and impacts of generative AI, particularly its environmental costs. Through a review of the available evidence on LLM’s carbon and water costs, we point out that generative AI technologies are distinctly resource intensive. We argue that the field must re-frame the scope of machine learning research and development to include carbon and other resource considerations across the lifecycle and supply chain, rather than setting these aside or allowing them to remain on the field’s margins.

Keywords: artificial intelligence, carbon emissions, climate change, environmental justice

06/11/2024: To preview this content, click below for the Just Accepted version of the article. This peer-reviewed version has been accepted for its content and is currently being copyedited to conform with HDSR’s style and formatting requirements.

©2024 Tamara Kneese and Meg Young. This article is licensed under a Creative Commons Attribution (CC BY 4.0) International license, except where otherwise indicated with respect to particular material included in the article.