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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.

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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