Certification Systems for Machine Learning: Lessons from Sustainability

Forthcoming (open access) in Regulation and Governance Abstract—The increasing deployment of machine learning systems has raised many concerns about its varied negative societal impacts. Notable among policy proposals to mitigate these issues is the notion that (some) machine learning systems should be certified. In this paper, we illustrate how recent approaches to certifying machine learning may be building upon the wrong foundations and examine what better foundations may look like. While prominent approaches to date have centered on networking standards initiatives led by organizations including the IEEE or ISO, we argue that machine learning certification may be better grounded in the very different institutional structures found in the sustainability domain. We first illustrate how policy challenges of machine learning and sustainability have significant structural similarities. Like many commodities, machine learning is characterized by difficult or impossible to observe credence properties, such as the characteristics of data collection, or carbon emissions from model training, as well as value chain issues, such as emerging core-periphery inequalities, networks of labor, and fragmented and modular value creation. We examine how focusing on networking standards, as is currently done, is likely to fail as a method to govern the credence properties of machine learning. While networking standards typically draw their adoption and enforcement from a functional need to conform in order to participate in a network, salient policy issues in machine learning issues benefit from no such dynamic. Finally, we apply existing research on certification systems for sustainability to the qualities and challenges of machine learning to generate lessons across the two, aiming to inform design considerations for emerging regimes.