Pubblicazioni

The European Commission has recognised the importance of a more innovation-oriented EU acquis, gradually exploring the ways in which EU rules can support innovation. The ‘innovation principle’ was introduced to ensure that whenever policy is developed, the impact on innovation is fully assessed. However, as further discussed in this Study, the exact contours of the innovation principle have been shaped very gradually within the context of the EU better regulation agenda: originally advocated by industry in the context of the precautionary principle, the innovation principle has gradually been given a more articulate and consistent role, which aims at complementing the precautionary principle by increasing the salience of impacts on innovation during all phases of the policy cycle. This Study presents an evaluation of the current implementation of the innovation principle, limited to two of its three components, i.e. the Research and Innovation Tool included in the Better Regulation Toolbox, and the innovation deals. As a preliminary caveat, it is important to recall that the implementation of the innovation principle is still in its infancy, and thus the Study only represents a very early assessment of the extent to which the innovation principle is being correctly implemented, and whether changes would be required to make the principle more effective and useful in the context of the EU better regulation agenda. The main finding is that the innovation principle has the potential to contribute to the quality and future-proof nature of EU policy, but that significant changes and effort will be needed for this potential to fully materialise. The most evident areas for improvement are related to the lack of a clear legal basis, the lack of a widely acknowledged definition, the lack of awareness among EU officials and stakeholders, and the lack of adequate skills among those that are called to implement the innovation principle. As a result of these problems, the impact of the innovation principle on the innovation-friendliness of the EU acquis has been limited so far. The Commission should clarify in official documents that the Innovation principle does not entail a de-regulatory approach, and is not incompatible with the precautionary principle: this would also help to have the principle fully recognised and endorsed by all EU institutions, as well as by civil society, often concerned with the possible anti-regulatory narrative around the innovation principle in stakeholder discussions. Apart from clarifications, and further dissemination and training, major improvements are possible in the near future, especially if the innovation principle is brought fully in line with the evolving data-driven nature of digital innovation and provides more guidance to the Commission on how to design experimental regulation, including inter alia so-called ‘regulatory sandboxes’. Finally, the Commission should ensure that the innovation principle is given prominence with the transition to the Horizon Europe programme, in particular due to the anticipated launch of ‘missions’ in key domains.

Artificial Intelligence (AI) is an area of research and technology application that can have a significant impact on public policies and services in many ways. In just a few years, it is expected that the potential will exist to free up nearly one-third of public servants’ time, allowing them to shift from mundane tasks to high-value work. Governments can also use AI to design better policies and make better decisions, improve communication and engagement with citizens and residents, and improve the speed and quality of public services. While the potential benefits of AI are significant, attaining them is not an easy task. Government use of AI trails that of the private sector; the field is complex and has a steep learning curve; and the purpose of, and context within, government are unique and present a number of challenges.

This article investigates the role of cost-benefit analysis during an antiregulatory period. The period since 2016 has seen several new developments, including the first vigorous use by Congress of its power to overturn recently issued regulations and the creation of novel deregulatory mechanisms layered on top of cost-benefit analysis. This period also contains important examples of sharply reversed CBAs, in which regulations that were said to have large net benefits under Obama are instead said to have net costs under Trump. The Trump Administration’s regulatory review initiatives focus heavily on costs, with limited attention to benefits. Case studies of three high-profile regulations show that the economic analysis of one is seriously defective, another admits to having severe limitations, and a third systematically reduces the scale of benefits. Some of these characteristics may be analytically defensible, others seemingly are not. It is even harder to connect Congress’s recent uses of the Congressional Review Act to either a concern about net benefits or even a desire to reduce the economic burdens of regulation. Thus, cost-benefit analysis seems overall a marginal part of current regulatory policymaking.

Cross-sectorial networks of regulators are starting to be adopted as a horizontal coordination mechanism. UKRN is one of the first horizontal regulators’ platform, starting its activity in 2014, and now focusing on extending sectorial disclosure-regulation by developing cross-sectorial performance scorecard aimed to compare and assess the customer experience of companies in different sectors (Antonio Manganelli, Rassegna Osservatorio Air n. 4/2019).

Effective antitrust enforcement requires that national authorities are empowered with strong intervention powers and real independence, both from the industry, as well as from the political power. In the field of European competition law, it is also necessary that the powers and the independence of national antitrust authoritiesare harmonised, in order to allow a real cooperation among them and between them and the European Commission. The Directive (EU) 2019/1 of the European Parliament and of the Council of 11 December 2018 is aimed at increasing the independence, resources and powers of NCAs and envisages more harmonisation of the national leniency programmes and reduced burdens on undertakings (Livia Lorenzoni, Rassegna Osservatorio Air n. 4/2019).

AGCOM, AGCM and the Italian Data Protection Authority jointly adopted policy recommendations to contain the power deriving from the use of big data in digital markets. The document, which is the result of a wide survey, not only proposes concrete solutions to counter the “power of data” of the great digital operators, but also outlines a stronger and more active role of the public sector in the regulation and free circulation of data (Matteo Falcone, Rassegna Osservatorio AIR n. 4/2019).

Introduzione. Strumenti (avanzati) di better regulation e realtà dei processi decisionali, di Gabriele Mazzantini e Nicoletta Rangone Capitolo primo. Il quadro internazionale ed europeo, di Laura Cavallo Capitolo secondo. L’AIR e la VIR, di Davide Zaottini Capitolo terzo. L’analisi d’impatto della regolazione sulla concorrenza, di Gabriele Mazzantini Capitolo quarto. Le consultazioni pubbliche, di Carolina Raiola Capitolo quinto. La trasparenza e l’accountability delle autorità indipendenti, di Emma Galli, Ilde Rizzo e Carla Scaglioni Capitolo sesto. La funzione di supervisione. Parlamento, autorità amministrative indipendenti e AIR: un connubio possibile, di Valerio Di Porto Capitolo settimo. Compliance ed enforcement della regolazione indipendente, di Livia Lorenzoni e Florentin Blanc Capitolo ottavo. Il contributo del Consiglio di Stato e dei TAR alla qualità della regolazione, di Simona Morettini

Artificial intelligence (AI) is becoming a part of our daily lives at a fast pace, offering myriad benefits for society. At the same time, there is concern about the unpredictability and uncontrollability of AI. In response, legislators and scholars call for more transparency and explainability of AI. This article considers what it would mean to require transparency of AI. It advocates looking beyond the opaque concept of AI, focusing on the concrete risks and biases of its underlying technology: machine-learning algorithms. The article discusses the biases that algorithms may produce through the input data, the testing of the algorithm and the decision model. Any transparency requirement for algorithms should result in explanations of these biases that are both understandable for the prospective recipients, and technically feasible for producers. Before asking how much transparency the law should require from algorithms, we should therefore consider if the explanation that programmers could offer is useful in specific legal contexts.

Also in the digital age, markets work properly as long as consumers are well informed. What is peculiar of the digital age is that consumers have become very fragile, also because firms can extensively manipulate the information that they produce and distribute to markets. Antitrust authorities may find their way to prosecute business manipulative conduct, as some rulings suggest. However, the enforcement of antitrust law is subject to precise circumstances and requires cumbersome proceedings, especially when dominant firms are involved. Therefore, a simpler and more widespread intervention is needed. Although over the years traditional disclosure regulation has showed its limits, today algorithmic analysis gives room to more effective forms of disclosure regulation. Therefore, the paper maintains that both regulators and antitrust authorities can use these new forms of disclosure regulation to perform better their functions. Thanks to algorithmic analysis, (a) regulators can provide consumers with targeted co-regulated disclosures; (b) while competition authorities, using their advocacy powers, can provide trustworthy rankings and reviews about firms’ ability to comply with antitrust and consumer protection laws.

The emergence of behavioral public administration has led to increasing calls for public managers and policy makers to consider predictable cognitive biases when regulating individual behaviors or market transactions. Recognizing that cognitive biases can also affect the regulators themselves, this article attempts to understand how the institutional environment in which regulators operate interacts with their cognitive biases. In other words, to what extent does the “choice architecture” that regulators face reinforce or counteract predictable cognitive biases? Just as knowledge of behavioral insights can help regulators design a choice architecture that frames individual decisions to encourage welfare‐enhancing choices, it may help governments understand and design institutions to counter cognitive biases in regulators that contribute to deviations from public interest policies. From these observations, the article offers some modest suggestions for improving the regulatory choice architecture.