Publications

Rather than proposing any single model for AI governance at this stage, the foregoing preliminary recommendations focus on the principles and functions to which any such
regime must aspire.
Over the coming months, we will consult — individually and in groups — with diverse stakeholders around the world. This includes participation at events tasked with discussing the issues in this report as well as engagement with governments, the private sector, civil society, and research and technical communities. We will also pursue our research, including on risk assessment methodologies and governance interoperability.
Case studies will be developed to help think about landing issues identified in the report in specific contexts. We also intend to dive deep into a few areas, including OpenSource, AI and the financial sector, standard setting, intellectual property, human rights, and the future of work by leveraging existing efforts and institutions.

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.

The Stanford Emerging Technology Review (SETR) is the first product of a major new Stanford technology education initiative for policymakers. The goal is to help both the public and private sectors better understand the technologies poised to transform our world so that the United States can seize opportunities, mitigate risks, and ensure that the American innovation ecosystem continues to thrive.

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.

In Republican and Democratic administrations, regulatory and funding decisions have both been made with close reference to benefit-cost analysis (BCA). With respect to regulation, there has been a great deal of scholarly discussion of BCA and its limits, but almost no attention has been paid to the role of BCA in government funding. That is a serious gap, not least in connection with climate-related risks, such as wildfire, drought, extreme heat, and flooding. In OMB Circular A-94, the Office of Management and Budget has long required applicants for federal funding to demonstrate that the benefits of their projects would exceed the costs. Under Circular A-94, efficiency-based BCA can produce results that fail to maximize welfare and that are also highly inequitable. The 2023 draft revision of Circular A-94, focused on welfare and equity, reflects an effort to incorporate new academic thinking over the past three decades, which is now—not uncontroversially—being brought directly into policy. At the same time, the new draft Circular A-94 raises fresh questions about how best to promote welfare, and to consider equity, in practice. Pressing issues involve the use of distributional weights in funding decisions and also the use of averages across populations, which might be seen as a form of distributional weighting. More broadly, the trajectory of this benefit-cost guidance, which predates the guidance for regulation and originally covered regulation, helps uncover the logic under which BCA has been operating and deeper challenges and tensions within BCA, in the past and going forward.

Growing volatility, uncertainty, complexity and ambiguity today present major challenges in policymaking. Anticipatory thinking and foresight are of utmost importance to help explore trends, risks and emerging issues, and their potential implications and opportunities in order to draw useful insights for strategic planning, policymaking and preparedness. This report is a part of the project ‘Anticipation and monitoring of emerging technologies and disruptive innovation’ (ANTICIPINNOV), a collaboration between the European Commission Joint Research Centre and the European Innovation Council (EIC). The findings include a set of 106 signals and trends on emerging technologies and disruptive innovations across several areas of application. It is based on a review of key reports on technology and innovation signals and trends produced by public and private bodies outside of the EU institutions. Its goal is to strengthen the EIC’s strategic intelligence capacity through the use and development of anticipatory approaches that will - among other goals – support prioritisation in innovation funding. Other insights were identified, namely those related to the scope of EIC programme manager portfolios.

The 2024 Commission Work Programme, adopted on 17 October 2023, puts a strong focus on simplifying rules for citizens and businesses across the European Union. This follows up on President von der Leyen’s commitment to reduce reporting requirements by 25%, in line with our strategy to boost the EU’s long-term competitiveness and to provide relief for SMEs.

The study submits that ‘One in, one out’ is a tool for less, not better, regulation and legislation, and, as such, it is not a suitable instrument for better law-making. To achieve effectiveness of legislation, the EU must reform its law-making policy holistically by placing the citizen at the core of its legislative communication. The EP must lead on and defend the citizens’ right to better legislation. To put this reform to effect, the JURI Committee must place itself at the centre of
deliberations, via a Working Group dedicated to Better Regulation, to assure a constant reflection on better regulation with the support of a network of European academic experts. This study was commissioned by the European Parliament’s Policy Department for Citizens’ Rights and Constitutional Affairs at the request of the JURI Committee.

This interim Report examines the factors behind recent AI developments, highlights the benefits offered by the technology, and identifies a series of challenges for policymakers. It is examined how the UK Government has responded, and how this compares to other countries and jurisdictions.

To aid policymakers in designing resilient and future-proof strategies, researchers have embarked on an innovative foresight process, envisioning four distinct versions of a sustainable EU in 2050 and the different pathways to get there starting from today. These visions and pathways shed light on the trade-offs and synergies, providing invaluable insights for the path before us. The results of this remarkable thought experiment, outlined in great detail in this recently published Science for Policy report, which fed into this year’s Strategic Foresight Report.

The European Commission presented the 2023 Strategic Foresight Report, which analyses how to put ‘sustainability and people's wellbeing at the heart of Europe's Open Strategic Autonomy' and suggests ten concrete actions to achieve this aim.
The 2023 report provides an overview of the challenges we face and proposes ten areas for action to achieve a successful transition. To equip policymakers with economic indicators which also consider wellbeing, it proposes to adjust Gross Domestic Product (GDP) to take account of different factors such as health and the environment.

This approach will bolster the EU's Open Strategic Autonomy and global standing in its pursuit of a resilient net-zero economy.

El sector público está empezando a incorporar herramientas basadas en inteligencia artificial, entre las que debemos destacar las destinadas a la lucha contra la corrupción y el fraude como una de las actividades de las administraciones más proclives al uso de herramientas basadas en inteligen- cia artificial. En el presente estudio se analiza cómo la unión de ciencias del comportamiento e inteligencia artificial pueden ser muy positivas para un adecuado control preventivo de la corrupción en la actuación de los servi- dores públicos, pero también abre la puerta a determinados riesgos propios del empleo de ambas técnicas (ciencias del comportamiento e inteligencia artificial), que deben tenerse en cuenta para así poder corregirlos.

Regulatory experimentation tools are used to test new economic, institutional and technological approaches, and legal provisions, outside of existing regulatory structures. This report focuses on regulatory sandboxes, where authorities engage firms to test innovative products or services that challenge existing legal frameworks. Participating firms obtain a waiver from specific legal provisions or compliance processes to innovate. This report presents lessons learnt from fintech regulatory sandboxes and their positive impacts, such as fostering venture capital investment in fintech start-ups. It covers challenges and risks involved in implementing regulatory sandboxes and their testing processes. This report presents policy considerations for AI regulatory sandboxes, including institutional interdisciplinary cooperation and the need to build up AI expertise within regulatory authorities. Findings underline the need for regulatory interoperability and the role that trade policy can play. Finally, it discusses the need for comprehensive criteria to determine sandbox eligibility and assess trials, and the potential impact of sandboxes on innovation and competition.

Markets are being populated with new generations of pricing algorithms, powered with artificial intelligence (AI), that have the ability to autonomously learn to operate. This ability can be both a source of efficiency and cause of concern for the risk that algorithms autonomously and tacitly learn to collude. In this paper we explore recent developments in the economic literature and discuss implications for policy.