Takeaways

There needs to be full transparency and comprehensive reporting on resource consumption and environmental consequences of generative AI. The currently limited standardized reporting on AI’s environmental impact makes it difficult to track and mitigate its escalating demands. AI companies must be required to publicly disclose the electricity, water, and emissions footprint of AI models, including off-site (scope-2) and supply chain (scope-3) usage in reporting.

We affirm the five demands of the Green Screen Coalition’s statement Within Bounds: Limiting AI’s environmental impact. You should, too.

Energy efficiency is insufficient progress. While energy efficiency improvements and the strategic scheduling of operations can reduce environmental impact, these often address only some aspects. Water and carbon footprints are complementary rather than interchangeable when addressing environmental impact, as optimizing for one may worsen the efficiency of the other ¹. Environmental impact needs to be addressed holistically: Addressing the water footprint along with the carbon footprint, emissions, and other impacts is critical.

It’s important to remember that while the water or electricity consumption per request may decrease in the future, the total water and energy consumption is likely to rise further as a result of the growing demand for AI services and the increasing scale of AI applications.

This should not be your responsibility. The burden of reducing AI’s environmental harms should not fall on individual users. Companies and policymakers must take responsibility for making decisions that prioritize environmental accountability.

What’s Next?

Head over to the Green Screen Coalition to learn more about a current initiative to limit AI’s climate impacts.

References