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This article will unpack the European Draft Act on Artificial Intelligence (AI), the first (both in Europe and in the world) far-reaching regulation in this domain. It categorizes AI systems in three (eventually four) levels of risk and it assigns to each level a particular legal framework, with its own limitations and obligations. The Draft Act was created for laudable purposes, namely to harmonize digital development with fundamental rights and European values. However, similar to many other ambitious projects, it falls short in many ways. As this article will show, several issues are incompletely regulated, and there are doubts about the exact scope and content of the legal solutions outlined in the draft. It also potentially overlaps with several other European norms, introducing the possibility of conflicts. Finally, the law’s focus on fundamental rights may come at the price of digital innovation, although critics claim that the Draft Act does not go far enough to protect such rights.

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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.

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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.

Better regulation is an agenda aiming at managing legislation across the different stages of the policy cycle. At the EU level, this agenda for reform has been handled as soft law with communications, reports, principles, and toolboxes. The ambiguity of the concept has created a policy arena where the EU institutions jockey for positions on the control of the lawmaking process. We then turn to the Member States. For their better regulation policies, they have chosen a combination of soft and hard instruments and different degrees of formalization. Tellingly, this variation shows the different views and assumptions on the efficiency of soft law as well as of the role played by legal and administrative traditions.

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