Publications

The European Union’s Digital Markets Act (DMA) initiative, which is set to introduce ex ante regulatory rules for “gatekeepers” in online platform markets, is one of the most important pieces of legislation to emanate from Brussels in recent decades. It not only has the potential to influence jurisdictions around the world in regulating digital markets, it also has the potential to change the business models of the wealthiest corporations on the planet and how they offer their products and services to their customers. Against that backdrop, this article provides an analysis of the aims of and principles underlying the DMA, the essential components of the DMA, and the core substantive framework, including the scope and structure of the main obligations and the implementation mechanisms envisaged by the DMA. Following this analysis, the article offers a critique of the central components of the DMA, such as its objectives, positioning in comparison to competition law rules, and substantive obligations. The article then provides recommendations and proposes ways in which the DMA – and other legislative initiatives around the world, which may take the DMA as an example – can be significantly improved by, inter alia, adopting a platform-driven substantive framework built upon self-executing, prescriptive obligations.

The Ada Lovelace Institute (Ada), AI Now Institute (AI Now), and Open Government Partnership (OGP) have partnered to launch the first global study to analyse the initial wave of algorithmic accountability policy for the public sector. As governments are increasingly turning to algorithms to support decision-making for public services, there is growing evidence that suggests that these systems can cause harm and frequently lack transparency in their implementation. Reformers in and outside of government are turning to regulatory and policy tools, hoping to ensure algorithmic accountability across countries and contexts. These responses are emergent and shifting rapidly, and they vary widely in form and substance – from legally binding commitments, to high-level principles and voluntary guidelines. This report presents evidence on the use of algorithmic accountability policies in different contexts from the perspective of those implementing these tools, and explores the limits of legal and policy mechanisms in ensuring safe and accountable algorithmic systems.

During the past decade, a small but rapidly growing number of Law&Tech scholars have been applying algorithmic methods in their legal research. This Article does it too, for the sake of saving disclosure regulation failure: a normative strategy that has long been considered dead by legal scholars, but conspicuously abused by rule-makers. Existing proposals to revive disclosure duties, however, either focus on the industry policies (e.g. seeking to reduce consumers’ costs of reading) or on rulemaking (e.g. by simplifying linguistic intricacies). But failure may well depend on both. Therefore, this Article develops a `comprehensive approach', suggesting to use computational tools to cope with linguistic and behavioral failures at both the enactment and implementation phases of disclosure duties, thus filling a void in the Law & Tech scholarship. Specifically, it outlines how algorithmic tools can be used in a holistic manner to address the many failures of disclosures from the rulemaking in parliament to consumer screens. It suggests a multi-layered design where lawmakers deploy three tools in order to produce optimal disclosure rules: machine learning, natural language processing, and behavioral experimentation through regulatory sandboxes. To clarify how and why these tasks should be performed, disclosures in the contexts of online contract terms and privacy online are taken as examples. Because algorithmic rulemaking is frequently met with well-justified skepticism, problems of its compatibility with legitimacy, efficacy and proportionality are also discussed.

This article argues that the poor design and implementation of experimental regulations and regulatory sandboxes can have both methodological and legal implications. First, the internal validity of experimental legal regimes is limited because it is unclear whether the verified positive or negative results are the direct result of the experimental intervention or other circumstances. The limited external validity of experimental legal regimes impedes the generalization of the experiment and thus the ability to draw broader conclusions for the regulatory process. Second, experimental legal regimes that are not scientifically sound make a limited contribution to the advancement of evidence-based lawmaking and the rationalization of regulation. Third, methodological deficiencies may result in the violation of legal principles (e.g., legality, legal certainty, equal treatment, proportionality) which require that experimental regulations follow objective, transparent, and predictable standards. This article contributes to existing comparative public law and law and methods literature with an interdisciplinary framework which can help improve the design of experimental regulations and regulatory sandboxes. This article starts with an analysis of the central features, functions, and legal framework of these experimental legal regimes. It does so by focusing on legal scholarship, policy reports, and case law on experimental regulations and regulatory sandboxes from France, United Kingdom, and The Netherlands. While this article is not strictly comparative in its methodology, the three selected jurisdictions illustrate well the different facets of experimental legal regimes. This article draws on social science literature on the methods of field experiments to offer novel methodological insights for a more transparent and objective design of experimental regulations and regulatory sandboxes.

Imagine that two doctors in the same city give different diagnoses to identical patients – or that two judges in the same court give different sentences to people who have committed matching crimes. Now imagine that the same doctor and the same judge make different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday, or they haven’t yet had lunch. These are examples of noise: variability in judgements that should be identical. In Noise, Daniel Kahneman, Olivier Sibony and Cass R. Sunstein show how noise produces errors in many fields, including in medicine, law, public health, economic forecasting, forensic science, child protection, creative strategy, performance review and hiring. And although noise can be found wherever people are making judgements and decisions, individuals and organizations alike commonly ignore its impact, at great cost. Packed with new ideas, and drawing on the same kind of sharp analysis and breadth of case study that made Thinking, Fast and Slow and Nudge international bestsellers, Noise explains how and why humans are so susceptible to noise and bias in decision-making. We all make bad judgements more than we think. With a few simple remedies, this groundbreaking book explores what we can do to make better ones.