This article aims to theorise corruption from a “macro” perspective. It elaborates upon a range of social sciences literatures, notably from criminology and political sciences, that have discussed various “macro”-level factors contributing to corruption, including market characteristics. Its most distinct contribution to the literature is in importing the concept of “systemic risk” – thus far chiefly examined in the financial regulation literature – to the analysis of corruption. It draws on the financial literature on systemic risk and macro-prudential regulation and supervision. In so doing, it elaborates upon concepts such as “network” and “contagion” as to theorise the effects of corruption on resource allocation. This is supplemented by reference to the legal canon of literature on market regulation.

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.

We shall first introduce the use of artificial intelligence (AI) in producing new intellectual creations, distinguishing approaches based on knowledge representation and on machine learning. Then we shall provide an overview of some significant applications of AI to the production of intellectual creations, distinguishing the extent to which they depend on pre-existing works, and the different ways in which such pre-existing works are used in the creative process. In addition, we shall discuss some methods to automatically assess the similarity of works and styles, in the context of AI technologies for text generation. Finally, we shall discuss the legal aspects of AI-reuse of copyrighted works, focusing on the rights of the authors of such works relative to the process and the outputs of AI.

This contribution explores the application of data science and artificial intelligence to legal research, more specifically an element that has not received much attention: the research infrastructure required to make such analysis possible. In recent years, EU law has become increasingly digitised and published in online databases such as EUR-Lex and HUDOC. However, the main barrier inhibiting legal scholars from analysing this information is lack of training in data analytics. Legal analytics software can mitigate this problem to an extent. However, current systems are dominated by the commercial sector. In addition, most systems focus on search of legal information but do not facilitate advanced visualisation and analytics. Finally, free to use systems that do provide such features are either too complex to use for general legal scholars, or are not rich enough in their analytics tools. In this paper, we motivate the case for building a software platform that addresses these limitations. Such software can provide a powerful platform for visualising and exploring connections and correlations in EU case law, helping to unravel the “DNA” behind EU legal systems. It will also serve to train researchers and students in schools and universities to analyse legal information using state-of-the-art methods in data science, without requiring technical proficiency in the underlying methods. We also suggest that the software should be powered by a data infrastructure and management paradigm following the seminal FAIR (Findable, Accessible, Interoperable and Reusable) principles.

The purpose of parliament is to present a discussion of policy in a fashion that will bring about a consensus that results in collective action. Such a collective action serves the common good of the state, although second-order effects and Pareto optimality is difficult – if not impossible – to obtain. Parliament attempts to address the second-best world in a socially optimal fashion. As such, accountability and scrutiny are the key tools through which such a body can address the challenges faced by a financial sector confronted with profound difficulties. Such accountability is of paramount importance to provide sustainable public trust in parliament.

Market interventions to protect public health are likely to be subject to EU law challenge as contrary to the rules on free movement. In the Scotch Whisky case the CJEU stressed the importance of defined public health objectives and supporting evidence in the analysis of whether interventions are justified as “appropriate” and “necessary”. This article considers the wider implications of this judgment for the application of the proportionality test in free movement cases and in the case of innovative interventions that are adopted on a complex evidence base. The article argues that the unusual development that Scotch Whisky made to the CJEU’s wider trend towards greater engagement with evidence should be treated with caution, and that it is possible for national courts to apply the new guidance on the role of evidence in the proportionality analysis with sensitivity. The article also argues that policymakers must now be more aware of how they frame innovative interventions and the evidence supporting them.

This study focuses on the dispute over Korea’s import bans and the additional testing and certification requirements for Japanese foods and analyses the relevant findings of the reports of the Panel and the Appellate Body of the World Trade Organisation’s dispute settlement mechanism (DSM). Although the Panel had found that Korea’s measures were inconsistent with the Agreement on the Application of Sanitary and Phytosanitary Measures (SPS Agreement), the Appellate Body reversed most of these findings. Korea notified that it had completed the implementation of the reports of the Panel and the Appellate Body. However, the DSM has failed to contribute in settling this dispute positively and meaningfully, since it remains uncertain whether Korea’s import bans are based on scientific principles. Furthermore, the Appellate Body failed to complete its legal analysis of the inconsistency of Korea’s measures based on fact-findings and the facts uncontested between both disputing parties.

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.