Publications

This guide for public officials and policy makers outlines eight models for institutionalising representative public deliberation to improve collective decision making and strengthen democracy. Deliberative bodies like citizens’ assemblies create the democratic spaces for broadly representative groups of people to learn together, grapple with complexity, listen to one another, and find common ground on solutions. Increasingly, public authorities are reinforcing democracy by making use of deliberative processes in a structural way, beyond one-off initiatives that are often dependent on political will. The guide provides examples of how to create structures that allow representative public deliberation to become an integral part of how certain types of public decisions are taken.

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.

In its latest proposals, the Digital Markets Act (DMA) and Digital Services Act (DSA), the European Commission puts forward several new obligations for online intermediaries, especially large online platforms and “gatekeepers.” Both are expected to serve as a blueprint for regulation in the United States, where lawmakers have also been investigating competition on digital platforms and new antitrust laws passed the House Judiciary Committee as of June 11, 2021. This Article investigates whether all stakeholder groups share the same understanding and use of the relevant terms and concepts of the DSA and DMA. Leveraging the power of computational text analysis, we find significant differences in the employment of terms like “gatekeepers,” “self-preferencing,” “collusion,” and others in the position papers of the consultation process that informed the drafting of the two latest Commission proposals. Added to that, sentiment analysis shows that in some cases these differences also come with dissimilar attitudes. While this may not be surprising for new concepts such as gatekeepers or self-preferencing, the same is not true for other terms, like “self-regulatory,” which not only is used differently by stakeholders but is also viewed more favorably by medium and big companies and organizations than by small ones. We conclude by sketching out how different computational text analysis tools, could be combined to provide many helpful insights for both rulemakers and legal scholars.

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