Publications

New technologies bring with them many promises, but also a series of new problems. Even though these problems are new, they are not unlike the types of problems that regulators have long addressed in other contexts. The lessons from regulation in the past can thus guide regulatory efforts today. Regulators must focus on understanding the problems they seek to address and the causal pathways that lead to these problems. Then they must undertake efforts to shape the behavior of those in industry so that private sector managers focus on their technologies’ problems and take actions to interrupt the causal pathways. This means that regulatory organizations need to strengthen their own technological capacities; however, they need most of all to build their human capital. Successful regulation of technological innovation rests with top quality people who possess the background and skills needed to understand new technologies and their problems.

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.

Technological innovation is changing private markets around the world. New advances in digital technology have created new opportunities for subtle and evasive forms of anticompetitive behavior by private firms. But some of these same technological advances could also help antitrust regulators improve their performance. We foresee that the growing digital complexity of the marketplace will necessitate that antitrust authorities increasingly rely on machine-learning algorithms to oversee market behavior. In making this transition, authorities will need to meet several key institutional challenges—building organizational capacity, avoiding legal pitfalls, and establishing public trust—to ensure successful implementation of antitrust by algorithm.

Evaluations of representative deliberative processes do not happen regularly, not least due to the lack of specific guidance for their evaluation. To respond to this need, together with an expert advisory group, the OECD has developed Evaluation Guidelines for Representative Deliberative Processes. They aim to encourage public authorities, organisers, and evaluators to conduct more comprehensive, objective, and comparable evaluations.
These evaluation guidelines establish minimum standards and criteria for the evaluation of representative deliberative processes as a foundation on which more comprehensive evaluations can be built by adding additional criteria according to specific contexts and needs.
The guidelines suggest that independent evaluations are the most comprehensive and reliable way of evaluating a deliberative process.
For smaller and shorter deliberative processes, evaluation in the form of self-reporting by members and/or organisers of a deliberative process can also contribute to the learning process.

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.

UNESCO member states adopt the first ever global agreement on the Ethics of Artificial Intelligence

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.

The better regulation guidelines set out the principles that the European Commission follows when preparing new initiatives and proposals and when managing and evaluating existing legislation.
The guidelines apply to each phase of the law-making cycle.

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.

Since the original publication of Nudge more than a decade ago, the title has entered the vocabulary of businesspeople, policy makers, engaged citizens, and consumers everywhere. The book has given rise to more than 400 “nudge units” in governments around the world and countless groups of behavioral scientists in every part of the economy. It has taught us how to use thoughtful “choice architecture”—a concept the authors invented—to help us make better decisions for ourselves, our families, and our society.

Now, the authors have rewritten the book from cover to cover, making use of their experiences in and out of government over the past dozen years as well as an explosion of new research in numerous academic disciplines. To commit themselves to never undertaking this daunting task again, they are calling this the “final edition.” It offers a wealth of new insights, for both its avowed fans and newcomers to the field, about a wide variety of issues that we face in our daily lives—COVID-19, health, personal finance, retirement savings, credit card debt, home mortgages, medical care, organ donation, climate change, and “sludge” (paperwork and other nuisances we don’t want, and that keep us from getting what we do want)—all while honoring one of the cardinal rules of nudging: make it fun!

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.

The book makes a significant contribution to the literature on regulatory governance and is a must-read for regulators—whether you are already using insights from the behavioural sciences in your regulatory practice or want to do so, or if you are sceptical about the whole nudge-buzz.

As intuitive statisticians, human beings suffer from identifiable biases, cognitive and otherwise. Human beings can also be “noisy,” in the sense that their judgments show unwanted variability. As a result, public institutions, including those that consist of administrative prosecutors and adjudicators, can be biased, noisy, or both. Both bias and noise produce errors. Algorithms eliminate noise, and that is important; to the extent that they do so, they prevent unequal treatment and reduce errors. In addition, algorithms do not use mental short-cuts; they rely on statistical predictors, which means that they can counteract or even eliminate cognitive biases. At the same time, the use of algorithms, by administrative agencies, raises many legitimate questions and doubts. Among other things, they can encode or perpetuate discrimination, perhaps because their inputs are based on discrimination, perhaps because what they are asked to predict is infected by discrimination. But if the goal is to eliminate discrimination, properly constructed algorithms nonetheless have a great deal of promise for administrative agencies.

Governments are created and run by humans, who can experience the same behavioural biases and barriers as individuals in society. Therefore, it makes sense to explore how behavioural insights (BI) can be applied to the governance of regulatory policy making, and not just to the design of regulations themselves. Applying BI can help improve the efficiency and effectiveness of the decision-making process, which can, in turn, help improve regulatory decisions. This paper maps the ways in which barriers and biases can affect the institutions, processes and tools of regulatory governance, with a focus on regulatory oversight bodies and regulatory management tools. It concludes with practical ways governments can translate these findings into research and reforms that can help future-proof regulatory policy making and ensure it is agile, responsive and fit for tackling important and complex policy challenges.