It takes too long to realize any climate change impacts from AI models. Damage done in AI development outweighs progress

For the time being, transparency obligations should fall on the organizations that are developing and deploying AI models—not on consumers who are only end users of AI models. That may change if large numbers of end users themselves end up training and developing their own models, causing a rapid expansion in AI-associated resource consumption and byproduct production. However, the question about where transparency obligations fall must be revisited regularly as AI technologies continue to develop rapidly and increasingly resource-intensive queries by users become possible. Crucially, hypothetical future affordances of AI must not be factored into the answers to these sustainability questions. For example, if the goal of an AI initiative is to help an end user reduce their carbon emissions, then that hypothetical future reduction must not be factored into the organization’s assessment of the carbon emissions of this AI initiative this year. Policymakers should promote the monitoring and reporting of accurate information, rather than define “good” answers to these questions and penalize companies that do not meet those benchmarks. The EU’s Sustainable Finance Disclosure Regulation framework, with its emphasis on the power of transparency to shape and amplify market forces, can serve as a model for such an approach. If reported data were gathered in a single, open-access database (perhaps analogous to the European Single Access Point), then regulators, investors, technology companies, nonprofits, and the general public would be able to reward progress toward sustainability goals, over various time horizons, through a variety of mechanisms. It will be important to have external auditors to ensure the credibility of reported data, as they have done for sustainable finance.