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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.

The Recommendation aims at further strengthening regulatory governance by helping to update and enhance relevant instruments, processes, and institutions. More precisely, it seeks to provide a conceptual framework and relevant guidance for using and adapting regulatory policy and governance in the face of the regulatory challenges and opportunities arising from innovation.

It is human to make mistakes. It is indisputably human to make mistakes while filling in tax returns, benefit applications, and other government forms which are often tainted with complex language, requirements, and short deadlines. However, the unique human feature of forgiving these mistakes is disappearing with the digitization of government services and the automation of government decision-making. While the role of empathy has long been controversial in law, empathic measures have helped public authorities balance administrative values with citizens’ needs and deliver fair and legitimate decisions. The empathy of public servants has been particularly important for vulnerable citizens (e.g., disabled individuals, seniors, underrepresented minorities, low income). When empathy is threatened in the digital administrative state, vulnerable citizens are at risk of not being able to exercise their rights because they cannot engage with digital bureaucracy. This Article argues that empathy, the ability to relate to others and understand a legal situation from multiple perspectives, is a key value of administrative law which should be safeguarded in the digital administrative state. Empathy can contribute to the advancement of procedural due process, equal treatment, and the legitimacy of automation. The concept of administrative empathy does not aim to create arrays of exceptions, imbue law with emotions and individualized justice. Instead, this concept suggests avenues for humanizing digital government and automated decision-making through the complete understanding of citizens’ needs. This Article explores the role of empathy in the digital administrative state at two levels: First, it argues that empathy can be a partial response to some of the shortcomings of digital bureaucracy. At this level, administrative empathy acknowledges that citizens have different skills and needs, and this requires the redesign of pre-filled application forms, government platforms, algorithms, as well as assistance. Second, empathy should also operate ex post as a humanizing measure which can help ensure that administrative decision-making remains human. Drawing on comparative examples of empathic measures employed in the United States, the Netherlands, Estonia, and France, the academic contribution of this Article is twofold: first, it offers an interdisciplinary reflection on the role of empathy in administrative law and public administration for the digital age that seeks to advance the position of vulnerable citizens; second, it operationalizes the concept of administrative empathy.

Artificial Intelligence (AI) is a peculiar case of General Purpose Technology that differs from other examples in history because it embeds specific uncertainties or ambiguous character that may lead to a number of risks when used to support transformative solutions in the public sector. AI has extremely powerful and, in many cases, disruptive effects on the internal management, decision-making and service provision processes of public administration. Over the past few years, the European Union and its Member States have designed regulatory policies and initiatives to mitigate the AI risks and make its opportunities a reality for national, regional and local government institutions. ‘AI Watch’ is one of these initiatives which has, among its goals, the monitoring of European Union’s industrial, technological, and research capacity in AI and the development of an analytical framework of the impact potential of AI in the public sector. This report, in particular, follows a previous landscaping study and collection of European cases, which was delivered in 2020. This document first introduces the concept of AI appropriation in government, seen as a sequence of two logically distinct phases, respectively named adoption and implementation of related technologies in public services and processes. Then, it analyses the situation of AI governance in the US and China and contrasts it to an emerging, truly European model, rooted in a systemic vision and with an emphasis on the revitalised role of the member states in the EU integration process, Next, it points out some critical challenges to AI implementation in the EU public sector, including: the generation of a critical mass of public investments, the availability of widely shared and suitable datasets, the improvement of AI literacy and skills in the involved staff, and the threats associated with the legitimacy of decisions taken by AI algorithms alone. Finally, it draws a set of common actions for EU decision-makers willing to undertake the systemic approach to AI governance through a more advanced equilibrium between AI promotion and regulation. The three main recommendations of this work include a more robust integration of AI with data policies, facing the issue of so-called “explainability of AI” (XAI), and broadening the current perspectives of both Pre-Commercial Procurement (PCP) and Public Procurement of Innovation (PPI) at the service of smart AI purchasing by the EU public administration. These recommendations will represent the baseline for a generic implementation roadmap for enhancing the use and impact of AI in the European public sector.

This paper considers briefly theoretical foundations of the links between regulation, “regulatory delivery” and compliance, and then a case study of construction safety regulation in Britain, and comparative data on occupational safety inspections and outcomes in Britain, Germany and France (European Union member states with generally comparable OSH regulations but very different regulatory delivery). It studies the use of behavioral approaches by the Health and Safety Executive (HSE) in Great Britain, where engagement with regulated entities, managers, workers and other stakeholders to improve OSH is central. It provides a brief analysis of how approaches to regulatory delivery based on behavioral insights can result in greater efficiency, increased compliance and more positive public outcomes. These approaches differ from a traditional “deterrence-based” conception of regulatory enforcement limited to finding and punishing violations. Evidence suggests that such behavior-focused regulatory delivery can be both more efficient and more effective.

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Digital platforms have strengthened their power more and more by collecting and exploiting data processed through algorithms. Economic and social interactions are increasingly shaped by the technology and software. Such a reliance on digital technologies has brought about the tendency of private subjects (but also of public institutions) to replace traditional norms and rules with regulations based on the code (code is law). From this perspective digital giants are no longer market participants. Rather they are market makers and exert regulatory control over the terms which define the positions of commercial and final users. Moreover they aspire to displace more governmental and public roles over time, performing functions and tasks normally vested in public authorities such as judicial bodies and courts. This shift in power towards private actors has led to an expanding privatization in the field of individuals’ rights as well. Algorithms are replacing the traditional functions of law embedding private values and interests in the technology. They represent the key of digital platforms’ power. Therefore regulators should ‘capture’ the algorithm to steer its effects and thanks to the technology be able to introduce regulatory principles into the design of the digital code.

It is generally agreed that digital markets have peculiar characteristics that are difficult to capture using traditional market analysis tools. For some, antitrust and regulatory authorities should intervene as little as possible and not hinder the flow of innovation taking place in these markets. However, the prevailing orientation, both in USA and in Europe, recognizes the need for policies to contrast the excessive power of the “web giants”. In this context, special antitrust rules and new conceptual tools are deemed necessary, starting with overcoming the traditional concept of the relevant market. The author believes that a similar result could also be achieved through an evolutionary interpretation of current antitrust rules.

This report looks at how data analysis techniques and information management tools can help make regulatory inspections more efficient through better risk analysis, targeting and co-ordination. It is based mainly on work conducted by the OECD in Italy (funded by EC DG REFORM) to pilot the use of machine learning techniques and data-driven analysis for risk assessment and to improve information systems integration. The pilots, undertaken in the regions of Campania, Lombardy and Trentino,, cover several regulatory domains – food safety, occupational safety, and environment. They show how better use and management of data can improve inspection systems even within a relatively short timeframe. The approach is based on improving the identification and rating of risk factors, so as to focus regulatory efforts on those businesses or establishments with the highest risks. This report was approved by the Regulatory Policy Committee at its 24th Session on 21st April 2021 and prepared for publication by the OECD Secretariat.