In Nudge (2008) Richard Thaler and Cass Sunstein suggested that public policy–makers arrange decision–making contexts in ways to promote behaviour change in the interest of individual citizens as well as that of society. However, in the public sphere and Academia alike widespread discussions have appeared concerning the public acceptability of nudgebased behavioural policy. Thaler and Sunstein's own position is that the anti–nudge position is a literal non–starter, because citizens are always influenced by the decision making context anyway, and nudging is liberty preserving and acceptable if guided by Libertarian Paternalism and Rawls’ publicity principle. A persistent and central tenet in the criticism disputing the acceptability of the approach is that nudging works by manipulating citizens’ choices. In this paper, we argue that both lines of argumentation are seriously flawed. We show how the anti–nudge position is not a literal non–starter due to the responsibilities that accrue on policy–makers by the intentional intervention in citizens’ life, how nudging is not essentially liberty preserving and why the approach is not necessarily acceptable even if satisfying Rawls’ publicity principle. We then use the psychological dual process theory underlying the approach as well as an epistemic transparency criterion identified by Thaler and Sunstein themselves to show that nudging is not necessarily about “manipulation”, nor necessarily about influencing “choice”. The result is a framework identifying four types of nudges that may be used to provide a central component for more nuanced normative considerations as well as a basis for policy recommendations.

In recent years the concepts of ‘nudge’ and ‘libertarian paternalism’ have become popular theoretical as well as practical concepts inside as well as outside academia. But in spite of the widespread interest, confusion reigns as to what exactly is to be regarded as a nudge and how the underlying approach to behaviour change relates to libertarian paternalism. This article sets out to improve the clarity and value of the definition of nudge by reconciling it with its theoretical foundations in behavioural economics. In doing so it not only explicates the relationship between nudges and libertarian paternalism, but also clarifies how nudges relate to incentives and information, and may even be consistent with the removal of certain types of choices. In the end we are left with a revised definition of the concept of nudge that allows for consistently categorising behaviour change interventions as such and that places them relative to libertarian paternalism.

Artificial intelligence (AI) is becoming a part of our daily lives at a fast pace, offering myriad benefits for society. At the same time, there is concern about the unpredictability and uncontrollability of AI. In response, legislators and scholars call for more transparency and explainability of AI. This article considers what it would mean to require transparency of AI. It advocates looking beyond the opaque concept of AI, focusing on the concrete risks and biases of its underlying technology: machine-learning algorithms. The article discusses the biases that algorithms may produce through the input data, the testing of the algorithm and the decision model. Any transparency requirement for algorithms should result in explanations of these biases that are both understandable for the prospective recipients, and technically feasible for producers. Before asking how much transparency the law should require from algorithms, we should therefore consider if the explanation that programmers could offer is useful in specific legal contexts.

In Europe, debate as to whether one should regulate chemicals based on intrinsic hazard or assessment of risk, or possibly a combination of both, has been gaining momentum. This article first provides a brief history of this risk versus hazard debate. Secondly, it examines how European regulators are currently handling the regulation of two chemical compounds, namely Bisphenol A and Deca BDE (a brominated flame retardant), based on forty-five expert interviews with regulators, policy makers and industry representatives in eight Member States, as well as with European Commission officials. The paper shows that there is no clear consensus as to when risk or hazard considerations should be the basis for regulatory decision-making, with wide discrepancies between Member States (e.g. the UK is overall more risk based than Sweden) and between regulatory agencies within Member States. The penultimate section puts forward a series of recommendations to help regulators and policy makers develop more consistent and science based regulations for Europe.

The world economy is sliding yet into another recession (having arguably barely recovered from the previous economic downturn) due to the worldwide pressures and tensions created by the COVID-19 pandemic.1 With most countries in the world under lockdown (or in similar situations), almost all food is now consumed in the household. Arguably, agricultural producers and the retail industry appear to be the best placed to weather the storm in order to respond to such a change in demand. However, this is overly simplistic. Recent news of empty shelves in supermarkets whilst dairy farmers have been forced to pour milk down the drain have gone viral.

Web-based financial intermediation on a peer-to-peer (P2P) basis will eventually prevail as an economically superior form of organisation compared to the traditional banking business model. P2P lending is the most popular type of crowdfunding, whereby an internet platform collects small amounts of funds from individuals in a crowd to finance collectively a larger loan to individuals or businesses. Unlike a commercial bank, the platform does not take risks through its own contractual positions. Whereas banks accumulate risks by taking positions on their balance sheet, platforms decentralise the risks by spreading them to their users. The emergence of financial platform businesses challenges legislators and regulators in several respects: the “easy way” of modifying existing finance and banking laws is inadequate; the internal organisation and knowledge of staff is not well suited to regulating crowdfunding; and capital mediation by crowdfunding platforms requires a different regulatory approach than banking. Finally, there is no doubt that P2P lending platforms will in the long run need a dedicated, single European regulatory framework.

The European Commission recently published the policy recommendations of its “High-Level Expert Group on Artificial Intelligence”: a heavily anticipated document, particularly in the context of the stated ambition of the new Commission President to regulate in that area. This article argues that these recommendations have significant deficits in a range of areas. It analyses a selection of the Group’s proposals in context of the governance of artificial intelligence more broadly, focusing on issues of framing, representation and expertise, and on the lack of acknowledgement of key issues of power and infrastructure underpinning modern information economies and practices of optimisation.

The EU's new approach to policy evaluation is characterised by a focus on closing the policy cycle (linking ex ante and ex post appraisal) and by applying evaluation to all types of policy intervention, whether expenditure or regulatory policy. This article analyses the main features and challenges of this new approach. It first studies the conceptual and interdisciplinary challenge of such an encompassing approach to evaluation. It then assesses the new approach in the light of four key objectives of ex ante and ex post appraisal; ensuring evidence and learning; accountability, transparency and participation; policy coherence; and reducing the regulatory burden.

New financial technology holds the promise of innovation and competition, challenging established products and services and frequently improving market processes. However, regulation of these new services faces a double challenge: to keep pace with innovation and facilitate new market entries while at the same time understanding and managing the regulatory risks that are involved.

At this stage, the existing EU regulatory framework is of little help: the bulk of the present body of financial regulation stems from a different time, with different regulatory problems in mind. EU regulation is also very slow to change and to adapt. Therefore, this paper proposes a regulatory “sandbox” – an experimentation space – as a step towards a regulatory environment where such new business models can thrive. A sandbox would allow market participants to test fintech services in the real market, with real consumers, but under the close scrutiny of the supervisor. The benefit of such an approach is that it fuels the development of new business practices and reduces the “time to market” cycle of financial innovation, while simultaneously safeguarding consumer protection. At the same time, a sandbox allows for mutual learning in a technical field which is sometimes poorly understood, both for firms and for the regulator. This would help to reduce the prevalent regulatory uncertainty for all market participants.

In the particular EU legal framework with various layers of legal instruments, the implementation of such a sandbox is not straightforward. In this paper, we propose a “guided sandbox”, operated by the EU Member States, but with endorsement, support, and monitoring by EU institutions. This innovative approach would be somewhat uncharted territory for the EU, and thereby also contribute to the future development of EU financial market governance as a whole.

Fake news is a symptom of deeper structural problems in our societies and media environments. To counter it, policymakers need to take into account the underlying, self-reinforcing mechanisms that make this old phenomenon so pervasive today. Only by taking a step back can we examine the vulnerabilities these fake news narratives exploit. This article provides a first taxonomy of anti-fake news approaches. It argues that proposed anti-fake news laws focus on the trees rather than the forest. As such, they will not only remain irrelevant but also aggravate the root causes fuelling the fake news phenomenon.

This article critically reflects upon the introduction of behavioural, ‘nudging’ approaches into UK policy making, the latest in a series of regulatory innovations. Initiatives have focused particularly upon correcting lifestyle risk behaviours, marking a significant continuity with previous ‘nannying’ policy. On the other hand, nudging represents a departure, even inversion of previous approaches that involved the overstating of risk, being based partly upon establishing a norm that bad behaviours are less, rather than more common than supposed. Despite substantive similarities, its attraction lies in the reaction against the former approach but must also be understood in the context of the economic crisis and a diminished sense of liberty and autonomy that makes intimate managerial intervention seem unproblematic. Problems are, in fact, substantial, as nudging is caught between the utility of unconscious disguised direction and the need to allow some transparency, thereby choice. Further, it assumes clear, fixed ‘better outcomes’ but encourages no development of capacity to manage problems, contradicting a wider policy intent to build a more responsible and active citizenry. More practically, nudging faces considerable barriers to becoming a successfully implemented programme, in the context of severe, Conservativeled austerity with which it is now associated.

Underground kitchens, public fridges and local meals at strangers’ homes offer an interesting and economically attractive alternatives to traditional channels of food distribution. The new practices of food sharing facilitated by technological development and using new social media combine an interest in modern engaged consumerism with a promise of more sustainable food chains and waste reduction. The main objective of this article is to map and analyse the risks and regulatory challenges posed by the variety of emerging sharing economy practices in the food sector. Drawing on the identification of the key features of sharing economy and the specificity of food in that context, this article discusses various forms, scales and motivations behind the existing food sharing initiatives. The article describes three main categories of food sharing models, namely harvest sharing, meal sharing and leftover sharing, and illustrates them with concrete examples of networks or platforms. Against this background, the article scrutinizes the major risks and challenges of food sharing, ranging from individual and public health and safety to market fragmentation. Reviewing the available regulatory options to address these risks, and juxtaposing them with the risks stemming out of regulatory intervention as such, the contribution calls for balanced approach to food sharing governance in the European Union.

Understanding of the causes and correlations for algorithmic decisions is currently one of the major challenges of computer science, addressed under an umbrella term “explainable AI (XAI)”. Being able to explain an AI-based system may help to make algorithmic decisions more satisfying and acceptable, to better control and update AI-based systems in case of failure, to build more accurate models, and to discover new knowledge directly or indirectly. On the legal side, the question whether the General Data Protection Regulation (GDPR) provides data subjects with the right to explanation in case of automated decision-making has equally been the subject of a heated doctrinal debate. While arguing that the right to explanation in the GDPR should be a result of interpretative analysis of several GDPR provisions jointly, the authors move this debate forward by discussing the technical and legal feasibility of the explanation of algorithmic decisions. Legal limits, in particular the secrecy of algorithms, as well as technical obstacles could potentially obstruct the practical implementation of this right. By adopting an interdisciplinary approach, the authors explore not only whether it is possible to translate the EU legal requirements for an explanation into the actual machine learning decision-making, but also whether those limitations can shape the way the legal right is used in practice.

The regulation of pesticides in the European Union (EU) relies on a network of hard law (legislation and implementing acts) and soft law (non-legally binding guidance documents and administrative and scientific practices). Both hard and soft laws govern how risk assessments are conducted, but a significant role is left to the latter. Europe’s pesticide regulation is one of the most stringent in the world. Its stated objectives are to ensure an independent, objective and transparent assessment of pesticides and achieve a high level of protection for health and environment. However, a growing body of evidence shows that pesticides that have passed through this process and are authorised for use may harm humans, animals and the environment. The authors of the current paper – experts in toxicology, law and policy – identified shortcomings in the authorisation process, focusing on the EU assessment of the pesticide active substance glyphosate. The shortcomings mostly consist of failures to implement the hard or soft laws. But in some instances the law itself is responsible, as some provisions can only fail to achieve its objectives. Ways to improve the system are proposed, requiring changes in hard and soft laws as well as in administrative and scientific practices.

This article deals with the civil liability issues that arise when self-driving vehicles are admitted to our streets. Firstly, it is explained why strict liability regimes for the vehicle holder, which are in place in several jurisdictions across Europe, are a workable foundation for ensuring compensation of the victim. However, accidents caused by highly automated vehicles will raise the question of the manufacturer's liability for a defective product. Accordingly, this article examines the effects of the circumstance that accidents occurring with self driving cars will no longer be caused by misconduct of the human driver, but often rather by a system malfunction. Thirdly, it is argued that preferably the manufacturer should be the risk bearer and that this shift in liability towards the manufacturer will result in an adequate allocation of liability.