Karen Yeung

TL;DR: It is argued that concerns about the legitimacy of these techniques are not satisfactorily resolved through reliance on individual notice and consent, touching upon the troubling implications for democracy and human flourishing if Big Data analytic techniques driven by commercial self-interest continue their onward march unchecked by effective and legitimate constraints.

TL;DR: In this paper, the authors provide a conceptual map of the field and an accessible and critical introduction to the subject of regulation for students coming to regulation for the first time, by adopting an interdisciplinary approach and emphasizing the role of law in its broader social and political context.

TL;DR: This study offers a taxonomy that identifies eight different forms of algorithmic regulation based on their configuration at each of the three stages of the cybernetic process, drawing upon insights from regulatory governance studies, legal critiques, surveillance studies, and critical data studies to highlight various concerns about the legitimacy of algorithmmic regulation.

TL;DR: The role played by a series of medical scandals in the U.K., occurring from the mid-1990s onwards, in ending a collegial model of self-regulation of the medical profession that had endured for 150 years is identified.

TL;DR: In 2019, the Organisation for Economic Co-operation and Development (OECD) Ministerial Council Meeting adopted the Recommendation on Artificial Intelligence, signed by all 36 OECD member countries and non-member countries Argentina, Brazil, Columbia, Costa Rica, Peru, and Romania.