Clean Data: Recursion as Pollution in Environmental AI

Authors

Joshua Fairfield, Washington and Lee University School of LawFollow

Document Type

Article

Publication Title

Oxford Intersections: AI in Society

Publication Date

3-20-2025

DOI

https://doi.org/10.1093/9780198945215.003.0024

Abstract

If “data is the new oil,” then corruption in the data used to train artificial intelligence (AI) constitutes a new form of pollution. Environmental AI has traditionally been discussed in terms of its indirect effects on the environment—the irony of burning power, processor cycles, and heat to produce solutions to stop and heal environmental damage. But there is a deeper problem. When environmental AI suggests interventions, its outputs are written onto the landscape. If that landscape is then read as data to retrain AI, there is a risk of model collapse and catastrophic forgetting, as the snake devours its own tail. This article discusses the difficulty in fit between current legal regimes governing AI and the use of AI in the environmental space and then further details the problems of model collapse in the context of environmental AI.

Recommended Citation

Joshua Fairfield, Clean Data: Recursion as Pollution in Environmental AI, Oxford Intersections: AI in Society (Mar. 20, 2025), https://doi.org/10.1093/9780198945215.003.0024.