Publications

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.

A growing body of literature discusses the impact of machine-learning algorithms on regulatory processes. This paper contributes to the predomi-nantly legal and technological literature by using a sociological-institutional perspective to identify nine organisational challenges for using algorithms in regulatory practice. Firstly, this paper identifies three forms of algorithms and regulation: regulation of algorithms, regulation through algorithms, and regulation of algorithms through algorithms. Secondly, we identify nine organisational challenges for regulation of and through algorithms based on literature analysis and empirical examples from Dutch regulatory agencies. Finally, we indicate what kind of institutional work regulatory agencies need to carry out to overcome the challenges and to develop an algorithmic regu-latory practice, which calls for future empirical research.

This article argues that if the EU is to recover its dented popularity among European publics, we need to build a European democratic ecosystem to nurture, scale and ultimate accommodate the daily competing claims of Europe’s citizens. To attain this objective, it presents and discusses three big ideas that are at the heart of the renewed EU ecosystem that we are calling for. These are: participation beyond voting; a transnational and inclusive public space; and, a democratic panopticon for greater accountability. Promisingly enough, these ideas already find reflection in the first batch of the citizens’ recommendations emerging from the Conference on the Future of Europe (CoFoE). Even if these recommendations still need to be refined through deliberation by the plenary of the CoFoE, they add up a clear and urgent message: let’s tap into our collective intelligence and democratic imagination to construct a pan-European public sphere by enhancing mutual connections, knowledge and empowerment between citizens across borders.