Big Data and Regulation (selected literature)
Strandburg K. J. (2020)
Rulemaking and Inscrutable Automated Decision Tools
Complex machine learning models derived from personal data are increasingly used in making decisions important to peoples’ lives. These automated decision tools are controversial, in part because their operation is difficult for humans to grasp or explain. While scholars and policymakers have begun grappling with these explainability concerns, the debate has focused on explanations to decision subjects. This Essay argues that explainability has equally important normative and practical ramifications for decision-system design. Automated decision tools are particularly attractive when decisionmaking responsibility is delegated and distributed across multiple actors to handle large numbers of cases. Such decision systems depend on explanatory flows among those responsible for setting goals, developing decision criteria, and applying those criteria to particular cases. Inscrutable automated decision tools can disrupt all of these flows. This Essay focuses on explanation’s role in decision-criteria development, which it analogizes to rulemaking. It analyzes whether, and how, decision tool inscrutability undermines the traditional functions of explanation in rulemaking. It concludes that providing information about the many aspects of decision tool design, function, and use that can be explained can perform many of those traditional functions. Nonetheless, the technical inscrutability of machine learning models has significant ramifications for some decision contexts. Decision tool inscrutability makes it harder, for example, to assess whether decision criteria will generalize to unusual cases or new situations and heightens communication and coordination barriers between data scientists and subject matter experts. The Essay concludes with some suggested approaches for facilitating explanatory flows for decision-system design.
Good Regulation and Public Policies Evaluation: selected literature
Carrigan C.; Febrizio M; Shapiro S. (2020)
Regulating Agencies: Using Regulatory Instruments as a Pathway to Improve BenefitCost Analysis
Scholars of regulation generally view the procedures that agencies must follow when promulgating rules as instruments by which political principals control bureaucratic agents. Much like political principals attempt to use procedural checks to constrain regulatory agencies actions, these same agencies employ various regulatory instruments to influence the decisions of private agents, especially firms. Despite the parallel nature of these principal-agent problems, few studies, if any, have looked at whether lessons from one can be used to inform the other. In this paper, we draw analogies between benefit-cost analysis (BCA)—a procedural control employed in the regulatory process—and three regulatory instruments that have similarities to BCA—performance standards, information disclosure requirements, and management-based regulation. We use lessons from research on the effectiveness of regulatory instruments to make predictions regarding the efficacy of BCA in various situations. Just as different regulatory instruments are appropriate for different regulatory contexts, the pathways by which BCA attempts to encourage better regulation may not all be applicable in every circumstance. We argue that such mutual exclusivity should inform how requirements for BCA are designed and that BCA’s emphasis on systematic analysis—the pathway most closely resembling management-based regulation—may offer the most promise for encouraging better rules.