Regulatory governance
Edited by Maggetti; Di Mascio; Natalini (2022)
Handbook of Regulatory Authorities (EE Publishing)
Featuring a comprehensive analytical collection of interdisciplinary research on regulatory authorities, this innovative Handbook presents the fundamental concepts, theories, practices, and empirical achievements and challenges in the contemporary study of regulatory authorities. Opening with a comparative overview of regulators across global regions, regulatory sectors, and regulatory types, the Handbook discusses the key regulatory conceptual issues of independence, politicization, and quality. Contributions from leading scholars and regulatory practitioners provide cutting-edge research on reputation, performance, and control in regulatory authorities. Chapters combine foundational theoretical concepts with empirical research to consider the emerging advances, challenges, and questions in the field, while also giving weight to critical examinations of complex and underexplored issues in research on regulatory authorities. Forward-thinking, the Handbook concludes by expanding its focus to analyse behavioural insights, innovation, agenda-setting, and new frontiers in regulation. With a cross-disciplinary approach, this all-encompassing Handbook will prove invaluable for students and scholars of politics, law, and economics with a regulatory governance perspective. Global in scope, it will be an essential point of reference for policy analysts, practitioners, and policymakers working in regulation and regulatory authorities.
Artificial Intelligence and new technologies regulation
C. Coglianese; A. Lai (2022)
Algorithm vs. Algorithm
Critics raise alarm bells about governmental use of digital algorithms, charging that they are too complex, inscrutable, and prone to bias. A realistic assessment of digital algorithms, though, must acknowledge that government is already driven by algorithms of arguably greater complexity and potential for abuse: the algorithms implicit in human decision-making. The human brain operates algorithmically through complex neural networks. And when humans make collective decisions, they operate via algorithms too—those reflected in legislative, judicial, and administrative processes. Yet these human algorithms undeniably fail and are far from transparent. On an individual level, human decision-making suffers from memory limitations, fatigue, cognitive biases, and racial prejudices, among other problems. On an organizational level, humans succumb to groupthink and free-riding, along with other collective dysfunctionalities. As a result, human decisions will in some cases prove far more problematic than their digital counterparts. Digital algorithms, such as machine learning, can improve governmental performance by facilitating outcomes that are more accurate, timely, and consistent. Still, when deciding whether to deploy digital algorithms to perform tasks currently completed by humans, public officials should proceed with care on a case-by-case basis. They should consider both whether a particular use would satisfy the basic preconditions for successful machine learning and whether it would in fact lead to demonstrable improvements over the status quo. The question about the future of public administration is not whether digital algorithms are perfect. Rather, it is a question about what will work better: human algorithms or digital ones.