Proposing the CASC: A Comprehensive and Distributed Approach to AI Regulation

Alex C. Engler is a Fellow at the Brookings Institution and an Associate Fellow at the Center for European Policy Studies, and teaches AI policy at Georgetown University, where he is an adjunct professor and affiliated scholar. Alina Constantin / Better Images of AI / Handmade A.I / CC-BY 4.0 Algorithmic systems are used to make many critical socioeconomic determinations—including in educational access, job discovery and hiring, employee management, financial services (such as mortgage pricing and property appraisal), rent setting, tenant screening, medical provisioning, and medication approval. This proliferation of algorithms is a defining issue of modern economic and social policy, with demonstrated implications for income equality, social mobility, health outcomes, and life expectancy.  Yet while the adoption of algorithms is nearly universal, the specifics of each application—the type of algorithms used, the data they manipulate, the sociotechnical processes they contribute to, and the risks they pose—vary greatly. Hiring algorithms have little in common with healthcare cost estimation algorithms or property valuation models. The role of algorithms in socioeconomic determinations is so manifold and diverse that it is not feasible, or even desirable, to create one set of algorithmic standards.  Therefore, a defining challenge of governing algorithms is enabling a regulatory approach that is comprehensive, but still enables application-specific rules and oversight by domain experts. In a new Brookings Institution report, I propose a novel solution to enabling comprehensive AI regulation through application-specific rulemaking. Proposing the Critical Algorithmic Systems Classification (CASC) To address this challenge, I am proposing a new…Proposing the CASC: A Comprehensive and Distributed Approach to AI Regulation