Publications

Consultations are key to gather evidence that informs rulemaking. When analysing the feedback received, it is essential for the regulator to appropriately cluster stakeholders’ opinions, as misclustering may alter the representativeness of the positions, making some of them appear majoritarian when they might not be. The European Commission (EC)’s approach to clustering opinions in consultations lacks a standardized methodology, leading to reduced procedural transparency, while making use of computational tools only sporadically. This paper explores how natural language processing (NLP) technologies may enhance the way opinion clustering is currently conducted by the EC. We examine 830 responses to three legislative proposals (the Artificial Intelligence Act, the Digital Markets Act and the Digital Services Act) using both a lexical and semantic approach. We find that some groups (like small and medium companies) have low similarity across all datasets and methodologies despite being clustered in one opinion group by the EC. The same happens for citizens and consumer associations for the consultation run over the DSA. These results suggest that computational tools actually help reduce misclustering of stakeholders’ opinions and consequently allow greater representativeness of the different positions expressed in consultations. They further suggest that the EC could identify a convergent methodology for all its consultations, where such tools are employed in a consistent and replicable rather than occasionally. Ideally, it should also explain when one methodology is preferred to another. This effort should find its way into the Better Regulation toolbox (EC 2023). Our analysis also paves the way for further research to reach a transparent and consistent methodology for group clustering.

In the era of digitalization, retail investors who buy/trade financial products through digital platforms often find themselves facing dark patterns, i.e. design models of online interfaces that exploit biases and emotions, leading them to make economic choices that are not always in their best interest. This contribution aims to explore the issue of dark patterns in the retail investment sector and the related regulatory outlooks. Therefore, it examines the perspectives that arise from the use of behavioral sciences and artificial intelligence to enhance the predominantly emerging command-and-control regulation in order to curb the phenomenon of dark pattern

Da tempo, le pubbliche amministrazioni fanno uso di tecnologie digitali, compresa l’intelligenza artificiale, nello svolgimento delle proprie funzioni. Tale fenomeno – unito all’impianto normativo di derivazione europea, volto a normare i processi di transizione digitale – impone un ripensamento dei principi e delle garanzie finora individuati dalla dottrina e dalla giurisprudenza e ispirati ai regimi giuridici tradizionali del diritto amministrativo. Il volume, muovendo dall’esame degli utilizzi attuali delle tecnologie digitali da parte dei pubblici poteri, ne ricostruisce le possibili nuove regole.

Abstract
Contemporary politics require a swift and responsive policy-making approach to respond to emergency situations quickly and to anticipate them in a strategic manner. Maintaining decision-making agility in times of urgency makes it necessary to think ahead of stress-impacting situations. To increase systemic resilience, policies must be designed in an adaptive way. Strategic options, futurity of policies and potential futures must be elaborated and kept in sight when developing policies. Anticipatory governance and strategic foresight activities are elements of such an adaptive governance mode. To transform decision-making in a future-oriented way, these forward-planning elements need to be institutionalised to support governance resilience. This article asks how anticipatory governance and strategic foresight are embedded within EU multilevel governance. It investigates the EU Better Regulation Agenda to understand how anticipatory governance already contributes to EU governance. Additionally, it discusses how the institutionalisation of strategic foresight can contribute to EU anticipatory governance.

Regulatory sandboxes, controlled regulatory environments for the testing of novel products or processes, have garnered an increasing amount of attention over the last decade. More recently, regulatory sandboxes have been presented as innovation-friendly instruments. This article contends that fostering responsible innovation through regulatory sandboxes presents significant challenges. First, there is no consensus on what the advancement of innovation entails, how to achieve it, and what the role of regulations and regulatory sandboxes should be in it. Second, there is a lack of clarity regarding the definition and functioning of regulatory sandboxes. Third, there is a risk of regulatory capture due to the close collaboration between regulators and regulatees. Drawing on Italy’s initial experiences with general and sector-specific regulatory sandboxes and existing scholarship on experimental regulatory instruments, this article contributes to the ongoing debate on regulation and innovation by critically examining the interplay between regulatory sandboxes and the promotion of responsible innovation. Furthermore, it explores the impact of regulatory sandboxes on the evolving collaborative dimensions of public law and provides policymakers and regulators with actionable insights for navigating this innovative regulatory tool.

Grand challenges are shaping twenty-first-century politics. Threats connected to health, climate, demographics and welfare are increasingly intruding on the lives of citizens. Still, governments are
often found off-guard, and policymakers need strategies grounded in longer-term perspectives. Strategic foresight (SF) helps us to design and shape policies to prepare to withstand shocks, anticipating and adapting to changes. However, as governments work towards embedding SF into their policymaking processes, the empirical evidence suggests that applications are still piecemeal and predominantly limited to the agenda-settings and policy formulation stages. In this article, we argue that to drive anticipatory governance, foresight needs to be applied at all stages of the policy cycle, including in evaluating policies to draw lessons for future interventions. We maintain that considering SF systemically throughout the policymaking cycle, from agenda setting to evaluation, strengthens anticipatory governance.

Il numero di ottobre della Rassegna trimestrale dell'Osservatorio AIR propone analisi sullo sviluppo di diversi strumenti della qualità della regolazione, sia relativamente alla metodologia, sia relativamente alla loro attuazione.
Nei primi tre contributi si commentano altrettante recenti analisi OCSE su tre rilevanti strumenti: il test PMI, le consultazioni e le sandboxes.
Nei restanti due commenti ci si concentra sull'aspetto attuativo, con un contributo di analisi sullo stato dell'arte dell'AIR e della VIR a livello nazionale (con un commento alla Relazione annuale del DAGL al Parlamento sull'applicazione di AIR e VIR) e sulla sperimentazione che il Friuli-Venezia Giulia ha effettuato in tema di enforcement, relativamente ai controlli nel settore lattiero-caseario.

The use of AI in the public sector is emerging around the world and its spread affects the core States functions: the administrative, the judiciary, and the legislative. Nevertheless, a comprehensive approach to AI in the life-cycle of rules - from the proposal of a new rule to its implementation, monitoring and review- is currently lacking in the rich panorama of studies from different disciplines. The analysis shows that AI has the power to play a crucial role in the life-cycle of rules, by performing time-consuming tasks, increasing access to knowledge base, and enhancing the ability of institutions to draft effective rules and to declutter the regulatory stock. However, it is not without risks, ranging from discrimination to challenges to democratic representation. In order to play a role in achieving law effectiveness while limiting the risks, a complementarity between human and AI should be reached both at the level of the AI architecture and ex post. Moreover, an incremental and experimental approach is suggested, as well as the elaboration of a general framework, to be tailored by each regulator to the specific features of its tasks, aimed at setting the rationale, the role, and adequate guardrails to AI in the life-cycle of rules. This agile approach would allow the AI revolution to display its benefits while preventing potential harms or side effects.