Stefaan Verhulst

OECD: “Measuring digital transformation is a key component of designing and implementing evidence-based policies. Yet measuring the digital parts of the economy is complex, in part because digital technologies and data are everywhere to some extent, rendering the notion of a siloed “digital economy” obsolete. Key challenges to measuring digital transformation include improving the international comparability of priority indicators and ensuring that statistical systems are flexible and responsive to the introduction of new and rapidly evolving concepts driven by digital technologies and data. Looking ahead, the challenge for the statistical community is to design new and interdisciplinary approaches to data collection and analysis, and to strengthen data infrastructure capabilities. Moreover, partnerships with the private sector and engagement with stakeholders to bring reliable and representative data that is gathered with trust into the policymaking process is an important overarching objective.

To address these challenges, it is important to not only identify common priorities (i.e. what to measure) but also common approaches (i.e. how to measure). The OECD Going Digital Measurement Roadmap 2026 (the Roadmap) aims to support and encourage a co-ordinated approach to digital measurement activities among key actors in the international statistical system. It includes ten actions aimed at advancing the capacity of countries to monitor digital transformation and its impacts. The Roadmap reflects a recognition that national statistical systems need to adapt and expand to adequately reflect the digitalisation of our economies and societies, with disaggregated data providing an evidence base from which to identify where digital divides exist and those who are most at risk from the disruption technological change brings. It also highlights the need for new, complementary data infrastructures capable of monitoring digital activities and data flows on a timely basis wherever they happen. The ten actions are outlined below…(More)”.

Article by Nana Kajaia and Tuntufye Ntaukira: “Digital wallets are becoming commonplace, often used for digitally storing payment cards instead of physical cards or cash. But beyond payments, as digital public goods with the right safeguards, digital wallets can enable individuals to reliably prove their eligibility for social protection benefits in times of need, securely share health records during an emergency, or promptly provide a certified document needed for a prospective employer.  

Whenever these digital forms are recognized and integrated across systems, they can significantly increase access to public and private services, enhancing people’s lives and livelihoods. This was the theme of UNDP’s recent Digital X 3.0 knowledge-exchange webinar on strengthening human security through digital public goods, organized in partnership with the Government of Japan. 

The discussions underscored how digital wallets as a core part of a country’s digital public infrastructure can unlock new opportunities for strengthening human security, across services, institutions and borders.  

Malawi and Argentina: Overcoming barriers to accessing critical services  

In many countries, people still tend to carry around printouts of essential documents and stand in queues for hours to confirm information that oftentimes already exists digitally. 

• Imagine a farmer in Malawi having to repeatedly submit physical documentation to show proof of land ownership to pay land taxes, because the national identity, agricultural, and financial systems in his country are not integrated. 
• Imagine a pregnant woman in Argentina trying to access maternal health services in a local clinic, but she is unable to provide a physical identification card that matches the name on her insurance card during an emergency visit…(More)”.

Article by Daniel Sachs: “Most commentaries on democratic erosion focus on the supply side of the equation – the strongmen and new doctrines, blocs, or geopolitical arrangements disrupting domestic politics and the rules-based international order. While important, this perspective ignores the demand that is driving current political trends.

…Proliferating wars and shaky alliances are hallmarks of today’s brutal new political reality, one that would have been unimaginable a decade ago. But the geopolitical rupture currently underway is no accident of history, nor is it simply the result of strongmen, weak institutions, or a sudden loss of restraint. It mirrors something more fundamental: the social soil of our societies. Politics does not occur in a vacuum. It grows out of lived experience, reflecting whether people feel secure, respected, and optimistic about a shared future…(More)”.

Article by byEdoardo Alberto Vigano and Paolo Gambacciani: “…To understand Italy’s approach to this issue, it is useful to look beyond the national context. So far, the adoption of AI in parliaments has been concentrated mainly in highly developed countries and has not been accompanied by a shared regulatory framework. The result is a fragmented landscape in which technological development and regulation are largely shaped by individual parliaments or EU institutions.

In practice, each parliament is adopting one or more AI tools according to internal priorities, with potentially significant implications for institutional organisation and the conduct of democratic deliberation.

Some applications are designed for internal use, supporting parliamentary staff, MPs and legislative committees. Others are outward-facing, aiming to enhance transparency, accessibility and citizen participation.

Some tools affect the legislative process directly; others primarily reshape the relationship between parliament and citizens. Current examples range from AI-assisted transcription and automated classification of debates and parliamentary activities, to automated sequencing of votes on amendments, drafting support and admissibility checks, natural-language search of parliamentary documents, and tools intended to synthesise public sentiment around bills under discussion. These examples suggest that AI is not merely a neutral administrative upgrade. It can reshape parliamentary power and practice, particularly when adoption concentrates on a specific class of tools.

Strategic choices in AI adoption

International cases illustrate how AI deployment may reflect strategic choices about parliament’s institutional role.

The Chilean Congress, for example, through its Caminar platform, has prioritised simplifying legislative activity by supporting the drafting of bills and amendments. By contrast, Brazil’s experience with initiatives such as Brasil Participativo has focused on strengthening popular participation, developing participatory AI solutions.

It is therefore unsurprising that the Inter-Parliamentary Union (IPU), which represents parliaments worldwide, has recently stressed that before adopting AI tools, parliaments should clarify the institutional role they intend to play in the future, particularly in relation to deliberation and the balance between parliament and government.

The IPU outlines three possible trajectories for representative assemblies:

• AI-Augmented Assembly: AI enhances human judgement while democratic primacy is preserved; AI acts as a “co-pilot” rather than replacing human decision-making.
• Data-Driven Legislature: AI becomes central to decision-making, with political deliberation increasingly displaced by predominantly evidence-based processes.
• Shadow Legislature: AI capabilities are concentrated within the executive branch, leaving parliaments structurally disadvantaged in managing emergencies, analysing complex dossiers and engaging citizens…(More)”.

Chapter by Anna De Liddo, Lucas Anastasiou, and Simon Buckingham Shum: “…introduces the concept of Collective Intelligence for Deliberative Democracy (CI4DD). We propose that the use of computational tools, specifically artificial intelligence to advance deliberative democracy, is an instantiation of a broader class of human-computer system designed to augment collective intelligence. Further, we argue for a fundamentally human-centred design approach to orchestrate how stakeholders can contribute meaningfully to shaping the artifacts and processes needed to create trustworthy DD processes. We first contextualise the key concepts of CI and the role of AI within it. We then detail our co-design methodology for identifying key challenges, refining user scenarios, and deriving technical implications. Two exemplar cases illustrate how user requirements from civic organisations were implemented with AI support and piloted in authentic contexts…(More)”.

Book by Nasim Afsar: “It was built to react and manage illness only after symptoms occur and it’s not even succeeding at that: chronic disease rates climb relentlessly, outcomes worsen year after year, and health and care grow more expensive. Worse still, the person who should be at the center is treated as a vessel for their illness, only valued for the revenue they generate.

Meanwhile, healthcare systems, payers, pharmaceutical companies, device manufacturers, and technology firms compete fiercely for their growth and survival. But no one is competing to make you healthier. We’ve reached a breaking point where the system doesn’t just fail; it actively harms people through preventable suffering and bankrupts families through inexcusable waste.

Enter Intelligent Health. A fundamental reimagining of health and care around a different center of gravity: you. It begins by unifying all your health and care data—clinical, behavioral, environmental, genetic—to see the complete picture of what shapes your health. It applies artificial intelligence to transform that data into actionable insight for today and predictive foresight about the future, catching problems before they cascade into crises. Most radically, it aligns the entire ecosystem of health and care around you.

Today, consumer health solutions multiply while outcomes stagnate. Costs spiral while access shrinks. Technology advances while coordination collapses. Intelligent Health offers what incremental reform cannot: a fundamental reimagining of health and care to create a system that is more human, more intelligent, and built to advance health…(More)”

Article by Clive Thompson: “…Computer programming has been through many changes in its 80-year history. But this may be the strangest one yet: It is now becoming a conversation, a back-and-forth talk fest between software developers and their bots.

This vertiginous shift threatens to stir up some huge economic consequences. For decades, coding was considered such wizardry that if you were halfway competent you could expect to enjoy lifetime employment. If you were exceptional at it (and lucky), you got rich. Silicon Valley panjandrums spent the 2010s lecturing American workers in dying industries that they needed to “learn to code.”

Now coding itself is being automated. To outsiders, what programmers are facing can seem richly deserved, and even funny: American white-collar workers have long fretted that Silicon Valley might one day use A.I. to automate their jobs, but look who got hit first! Indeed, coding is perhaps the first form of very expensive industrialized human labor that A.I. can actually replace. A.I.-generated videos look janky, artificial photos surreal; law briefs can be riddled with career-ending howlers. But A.I.-generated code? If it passes its tests and works, it’s worth as much as what humans get paid $200,000 or more a year to compose.

You might imagine this would unsettle and demoralize programmers. Some of them, certainly. But I spoke to scores of developers this past fall and winter, and most were weirdly jazzed about their new powers.

“We’re talking 10 to 20 — to even 100 — times as productive as I’ve ever been in my career,” Steve Yegge, a veteran coder who built his own tool for running swarms of coding agents, told me. “It’s like we’ve been walking our whole lives,” he says, but now they have been given a ride, “and it’s fast as [expletive].” Like many of his peers, though, Yegge can’t quite figure out what it means for the future of his profession. For decades, being a software developer meant mastering coding languages, but now a language technology itself is upending the very nature of the job…(More)”.

Paper by Ajay K. Agrawal, John McHale & Alexander Oettl: “We explore the impact of artificial intelligence (AI) on the knowledge production function. We characterize AI as a tool, not for full automation but rather for augmentation through enhanced search over combinatorial spaces. This leads to increased scientific productivity. We decompose knowledge production into a multi-stage process to shed light on the “jagged frontier” of AI in science, revealing differential returns to different tools across domains (e.g., data-rich biology vs. anomaly-sparse physics) and workflow stages (e.g., strong design aids like AlphaFold vs. subtler question generation tools). We treat human judgment as indispensable for tasks involving abductive inference, contextual nuance, and trade-offs, particularly in data-sparse environments. Drawing on a task-based model that distinguishes “ordinary” from AI-expert scientists, we describe how exogenous improvements in AI yield nonlinear productivity gains amplified by the share of scientists that are AI-experts to underscore the role of AI complements like skills training and organizational design…(More)”.

Article by Anwar Aridi, Henning Kroll: “Today, emerging economies must contend with a shifting landscape of global technology governance. As fields such as artificial intelligence, quantum computing, and synthetic biology become subject to new national security concerns, access to knowledge and participation in the shaping of international trade, finance, and standards regulations are increasingly contested. Moreover, established conventions of international economic and technological collaboration are being challenged, and old partnerships may no longer be relied upon. Yet certain areas—notably health and climate—remain less fraught within the context of geopolitics, offering entry points for emerging economies even during times of heightened global tension. A recent example can be found in the successes of global effort to produce vaccines during the COVID-19 pandemic, even in a politically contested environment.

In this new geopolitical landscape, technology dependence still signals vulnerability. Emerging economies under political pressure to reduce dependence may encourage domestic production of goods to replace more advanced, imported ones: a policy dubbed import substitution. But, if pursued prematurely, before domestic producers are ready to compete, import substitution can backfire, allowing a small number of established firms to capture state resources and hurting domestic consumers.

These risks are intimately related to political forces that shape how industrial policy priorities are chosen and the state’s capacity for implementing them. For these reasons, industrial policy must be embedded in transparent, accountable, and performance-based governance mechanisms. Emerging economies, much like developed ones, must avoid the pitfalls of politically driven resource allocation when the government becomes involved in steering the country’s economic future.

Even as today’s geopolitical complexities put added stress on emerging economies, a targeted strategy for industrial policy—as well as international collaboration—continues to be essential in enabling national economies’ transition toward more advanced stages of technological development. Countries cannot attempt to “go it alone” before attaining sufficient capacity. Yet, when collaboration becomes fraught with uncertainty, policies should carefully address how to invest resources to catch up with leading economies. Only by tailoring strategies to each stage of development, investing in knowledge institutions, and navigating the geopolitics of technological governance with agility can emerging economies secure a path toward technological sovereignty…(More)”.

OECD Policy Brief: “Shipbuilding underpins competitiveness, economic security, and higher energy efficiency. Yet across major shipbuilding economies, industrial policy decisions are frequently taken with incomplete, inconsistent, or poorly aligned evidence. Four structural data challenges stand out: inconsistent statistical definitions that undermine international comparability; limited visibility on where value is created and which factors drive productivity; delayed identification of emerging skills needs; and insufficient, non-standardised metrics to track technological upgrading and decarbonisation progress. Strengthening shipbuilding data is a strategic priority. Building on OECD tools such as the Structural Analysis (STAN) Database, Inter-Country Input-Output (ICIO) Tables, Trade in Value Added (TiVA) and the Ocean Economy Monitor, improved definitions, labour intelligence, and innovation indicators can support improved policy targeting, monitoring, and cross-country benchmarking — enabling more effective and resilient shipbuilding industrial policy…(More)”.