Stefaan Verhulst

Article by Karine Perset and Anna Pietikäinen: “…Six insights about AI regulatory sandboxes from around the globe

1. AI sandboxes are not uniform

• According to the Datasphere Initiative, three primary types of sandboxes are emerging worldwide, especially within the context of AI. Regulatory sandboxes: Collaborative processes where regulators work with innovators to test innovations under regulatory supervision.
• Operational sandboxes: Testing environments and infrastructure where data can be hosted and accessed in controlled conditions.
• Hybrid models: Combining regulatory oversight with operational capabilities, sometimes offering infrastructure and operational spaces for testing and experimentation (e.g., “supercharged sandbox” in the UK).

These models intervene at different phases of the policy and regulatory lifecycle. Some are employed before formal regulation to identify gaps and suggest necessary updates. Others operate during the development process, supporting iterative regulatory design. Some focus on helping understand legal obligations and ensure regulatory compliance, such as under the EU AI Act. Sector-specific sandboxes are also common, with countries adopting different approaches depending on regulatory priorities and institutional settings. Across these models, regulatory waivers are frequently used to enable experimentation under regulatory supervision. 

Several experimentation-related initiatives, such as regulatory testbeds, living labs, or policy prototyping, share certain features and objectives with regulatory sandboxes. What truly distinguishes sandboxes is that they are the most institutionalised form of regulatory experimentation, usually led by regulators and integrated with regulatory supervision..(More)”.

Article by Ian Leavitt: “Connecticut is one of many U.S. states increasingly using cross-sector data-sharing to improve public health outcomes, with the Prevention Data Portal a prime example of the state’s efforts. The portal demonstrates how state leadership buy-in, paired with expertise from nongovernmental partners, can overcome common barriers to cross-sector data-sharing and produce clear and meaningful information to help shape state policy—without requiring large new investments in data infrastructure or centralized control. Insights gained from Connecticut’s experience can inform the work of other state public health agencies as they look to expand cross-sector data-sharing through creative partnerships, simple use cases that show value early, and other avenues within their own states.

The Prevention Data Portal, launched in 2018 by Connecticut’s State Epidemiological Outcomes Workgroup (SEOW), houses data from local, state, and federal sources to advance health promotion and substance use prevention in the state. It provides free access to a wide range of data-driven products about populations in Connecticut, including epidemiological profiles, data stories using multiple streams of information, and infographics on mental health, substance use, suicide, gambling, and other public health topics. The portal is supported through federal block grant funding and partnerships between the SEOW and the Connecticut State Department of Mental Health and Addiction Services (DMHAS), the Center for Prevention Evaluation and Statistics (CPES) at UConn Health, and the Connecticut Data Collaborative (CTData).

In an ongoing collaboration with states to improve public health data, The Pew Charitable Trusts commissioned interviews with Connecticut officials and other participants in the conceptualization, creation, and use of the SEOW Prevention Data Portal. Those interviews produced several key takeaways…(More)”.

European Commission: “Open Research Europe (ORE) is the European Commission’s open access publishing platform for research funded by all EU programmes. Launched in 2021 to promote innovative, no-fee open access publishing, the platform is now preparing to enter a new phase.

Backed by a nearly €17 million budget for the period 2026-2031 and co-funded by the European Commission by up to €10 million, the new phase of ORE is set to begin operations as a collectively supported publishing service in the autumn of 2026, with CERN operating the platform. Leveraging on its success, the publishing service will be driven by national research organisations from 11 countries (Austria, France, Germany, Italy, the Netherlands, Norway, Portugal, Slovenia, Spain, Sweden, and Switzerland). In addition to researchers benefiting from EU grants, collective funding will also enable researchers from participating countries to publish without fees, thus expanding significantly author eligibility. ORE was conceived within the European Research Area (ERA)’s policy framework with the objective of ensuring open access to high-quality EU-funded research results, strengthening the free circulation of knowledge and maximising the impact of publicly funded research. In the five years since its launch, the platform has seen a steady growth and uptake among the research community, with more than 1,200 articles published and over 6,300 authors from more than 3,000 institutions worldwide.

The platform began its journey with a vision to redefine scholarly publishing, not only by offering researchers a no-fee open access platform, but also by enriching public knowledge and fostering trust in science through an innovative publishing model marked by rigorous and open post-publication peer review…(More)”.

Paper by Stefaan Verhulst: “As data sharing and reuse become central to scientific discovery, artificial intelligence, and public decision-making, the challenge of data governance has shifted from a primarily technical problem to one of institutional design. Over the past decade, a wide range of governance models for data collaboration have emerged-including data trusts, data commons, data cooperatives, data intermediaries, data unions, data sandboxes, and data spaces. These models are often presented as competing institutional solutions to the problem of responsible data sharing. This paper argues instead that such models represent distinct governance responses to different structural challenges within data ecosystems. 

Building on the concept of data collaboratives-which I introduced in 2017 as cross-sectoral arrangements for responsible data reuse in the public interest-I propose a purpose-driven typology that identifies seven governance archetypes and the specific coordination problems they address, including transaction costs, power asymmetries, legitimacy deficits, collective governance needs, ownership inequality, systemic uncertainty, and scaling complexity. 

I argue that the question is not which governance model is normatively superior, but rather which model is fit for purpose within particular institutional contexts. The paper concludes by introducing a functional theory of data collaboration centered on institutional orchestration, whereby multiple governance arrangements coexist and evolve within polycentric data ecosystems. In this framework, strategic data stewardship becomes essential for diagnosing governance needs and sequencing institutional responses that enable responsible and sustainable data reuse…(More)”.

Article by Geoff Mulgan: “…The roots of the word intelligence (drawing on the Latin words ‘inter’ and ‘legere’. to choose between) are a reminder that intelligence is not processing speed but rather the ability to choose between options. As such intelligence only makes sense in relation to purposes and environments. It doesn’t float freely, even if some of the tools it uses may be generic. This is why real-life intelligence is so varied, and so often an assembly or hybrid of multiple elements, fitted to ecological niches.

The autonomous vehicle is a good example. It uses multiple tools to sense, navigate and decide rather than a single AI, and it often requires adjustments for different environments. Everyday uses of AI in teams also illustrate the point. These usually mobilise multiple agents or tools to help them think, sometimes relying heavily on the technologies, sometimes putting them to one side.

Humans of the future may be equally hybrid. Many will have implants and prosthetics, living as part of networks of varying degrees of humanness, cyborgs for whom the boundaries of the human may be ever harder to define.

The key point is that we need to think in a biological way rather than a mechanistic way: how AI shapes humans and how humans shape AI will be more like an ecosystem or a jungle than a super machine, more like a forest in which there are predators, diseases and periodic fires as well as harmony, rather than a factory production line.

Too often we try to squeeze AI into anachronistic categories, asking: is it conscious, self-aware or creative, or should it have rights… ?

I suspect that few of these categories will turn out to be adequate to the task of making sense of life with super-intelligent machines. Nor will AI solve the problems of running a society in a neat new maths. Precisely because intelligence is so inseparable from life, and the life-wishes of entities as varied as wasps and bears, humans and machines, there is no conceivable calculus that could capture them let alone make them commensurate. Money is commensurable. Value is not. Machines can be aligned. Sentient beings cannot be…(More)”.

Article by Martin Wolf: “The creation, communication and exploitation of knowledge are the defining abilities of human beings as social animals. These capacities, more than anything else, made them masters of the planet. This makes our tools of communication — from language to writing, printing, telecommunications, radio, television and now the internet — the defining technologies of their eras. Their invention and use have shaped not just what we can do at any time, but who we are.

New technologies of communication transform society. As the late Jürgen Habermas argued, liberal democracy, now in peril, was the child of the book, the pamphlet and the newspaper. The digital technologies of our time are similarly transformative. Unfortunately, along with many gains, they bring huge potential harms that today threaten the health of our societies. These harms are not theoretical; they are all too visible.

Knowledge is in the language of economics, a “public good”. This means that, if it is publicly available, it will be potentially available to everybody and also that everybody can have it without anybody being deprived of it: technically, it is “non-excludable” and “non-rival”. Knowledge, as is sometimes said, “wants to be free”. Indeed, given today’s technology the marginal cost of disseminating information is essentially zero.

Yet creating true information is not free at all. This creates a huge market failure: the creation and dissemination of reliable information is at an economic disadvantage relative to the creation and dissemination of fabrications. The public good of knowledge can readily turn into the public bad of confident ignorance or, worse, raging prejudice.

Thus, just like rivers or the air, the knowledge society shares and uses can become polluted. Worse, this can be a very profitable business. It is not hard to think of contemporary examples. Above all, if there is a market failure, competition alone will not cure it. Free speech is an important attribute of a free society. But, on its own, it does not guarantee reliable truth. Rivers of cheap lies can all too easily drown the costly truth…(More)”.

Article by Ezra Klein: “…Researchers have drawn a distinction between “cognitive offloading” and “cognitive surrender.” Cognitive offloading comes when you shift a discrete task over to a tool like a calculator; cognitive surrender comes when, as Steven Shaw and Gideon Mave of the University of Pennsylvania put it, “the user relinquishes cognitive control and adopts the A.I.’s judgment as their own.” In practice, I wonder whether this distinction is so clean: My use of calculators has surely atrophied my math skills, as my use of mapping services has allowed my (already poor) sense of direction to diminish further.

But cognitive surrender is clearly real, and with it will come the atrophy of certain skills and capacities, or the absence of their development in the first place. The work I am doing now, struggling through yet another draft of this essay, is the work that deepens my thinking for later.

In a thoughtful piece, the technology writer Azeem Azhar describes his efforts to safeguard “the space where ideas arrive before they’re shaped.” But how many of us will put in such careful, reflective effort to protect our most generative spaces of thought? How many people even know which spaces should be protected? For me, the arrival of an idea is less generative than the work that goes into chiseling that idea into something publishable. This whole essay began as a vague thought about A.I. and McLuhan. If I have gained anything in this process, it has been in the toil that followed inspiration.

The other thing I notice the A.I. doing is constantly referring back to other things it knows, or thinks it knows, about me. Sycophancy, in my experience, has given way to an occasionally unsettling attentiveness; a constant drawing of connections between my current concerns and my past queries, like a therapist desperate to prove he’s been paying close attention.

The result is a strange amalgam of feeling seen and feeling caricatured. Ideas I might otherwise have dropped keep getting reanimated; personal struggles I might otherwise move on from keep returning unexpectedly to my screen. I am occasionally startled by the recognition of a pattern I hadn’t noticed; I am often irked by the recitation of a thought I’m no longer interested in. The effect is to constantly reinforce a certain version of myself. My self is quite settled, but what if it wasn’t?

The A.I. knows me imperfectly, and so it overtorques on what it knows and ignores what it doesn’t. But there is much it can never know about me, and there is much I won’t share, or don’t even know about myself. I wonder whether deeper reliance on A.I. would desiccate those less legible aspects of myself, and it’s one reason I hold myself back. But I am in my 40s, and I still feel the shock of something new and strange when I reveal myself to these systems. I think the young will allow themselves to be known to their A.I.s in ways that will make their elders shudder…(More)”.

Article by Akis Karagiannis: “Earth Observation is far easier to access than it was a decade ago. Data once handled by a narrow set of agencies and specialist teams now circulate through open archives, cloud platforms, browser tools, and shared analytical environments. Those changes have widened entry, lowered some technical barriers, and made new forms of scrutiny possible. Yet public use still fails for more ordinary administrative reasons. Monitoring programmes lose continuity, workflows never become a stable part of institutional operations, and technically available services sit idle when budgets tighten, procurement stalls, staff move on, or no organisation takes responsibility after release. Availability is only one condition of public use. On its own, it secures very little.

Part of the confusion lies in treating openness as a single condition. A sensing asset produces data. An access regime determines who can use them and on what terms. An operational layer turns them into alerts, maps, and monitoring outputs. Organisational uptake relies on ministries, agencies, NGOs, journalists, or community groups having authority, staff, methods, and routines for repeated use. Some arrangements add a further public-facing layer that keeps information available and inspectable across institutions and publics. Each part breaks down differently, and each draws on its own mix of budgets, contracts, stewardship, and administrative effort. An arrangement can look open on paper and remain thin in practice.

Markets can sustain some parts of this landscape. Firms will pay for bespoke analytics, tasking priority, premium delivery, or sector-specific products when the gains are direct and excludable. Public-facing uses are harder to fund that way. Regulatory oversight, early warning, environmental monitoring, and accountability produce benefits that spill across agencies and sectors, often appearing as avoided harm, better timing, or stronger scrutiny rather than revenue to a single buyer. Those gains are real, but they are difficult to capture through individual transactions. Public procurement and anchor demand therefore shape markets in ways private demand rarely will on its own.

That distinction helps separate cases often grouped together under the heading of openness. Carbon Mapper, MethaneSAT, and FireSat involve monitoring capabilities whose social return is easier to defend than to monetise. NICFI centres on purchased access to imagery already in orbit. SERVIR and Digital Earth Africa show what uptake requires inside institutions and regions. Global Forest Watch serves a different function, keeping shared evidence available across journalists, public agencies, NGOs, and researchers who would otherwise work from more fragmented ground. The economics of openness change at each point…(More)”.

Scan by Stefaan Verhulst and Begoña G. Otero: “As organizations work with more complex, real-time, and AI-enabled data environments, data governance can no longer be treated as a downstream compliance exercise. It needs to be designed across the full data life cycle: from planning and collection to processing, sharing, analysis, and use. That is the premise behind our new scan released today: Data Governance Innovations: Emerging Practices and Trends Across the Data Life Cycle.

Developed as a companion to our Q&A, What is Data Governance, this scan curates recent developments and maps them to the stages of the data life cycle where they are most relevant in practice: planning, collecting, processing, sharing, analyzing, and using data.

It curates innovations along the following interrelated dimensions:

Practices: new methods, tools, and governance arrangements that can be embedded into organizational operating models—such as privacy-enhancing technologies, data commons, agentic AI for discovery, social license, model cards, policy as code, data spaces, data collaboratives, data sandboxes, digital twins and benefit-sharing mechanisms, among others.

Structural Forces:  dynamics shaping the environment in which data governance decisions are made. These include the rapid deployment of AI and the emergence of agentic systems, increasing regulatory complexity (“regulatory densification”), evolving data sovereignty and cross-border constraints, and the growing “data winter” affecting access to and reuse of data for public interest purposes.

Cross-Cutting Issues: System-wide considerations that influence governance across the entire data lifecycle, including the integration of AI and data governance, the development of social license for data reuse, and alignment with digital public infrastructure.

Designed as a living document, the scan will continue to evolve as governance practices change…(More)”

Paper by Dimitrios Kalogeropoulos, Paul Barach, Andrea Downing, Stefaan Verhulst and Maryam B. Lustberg: “Healthcare services and data ecosystems remain fragmented, inequitable, and misaligned with the real-world needs of patients, clinicians, and public health systems. Existing pathways to patient-centred AI often lack contextual sensitivity and perpetuate disparities, limiting the transformative potential of AI to create personalised and inclusive care. This non-systematic narrative review examines three suitable pathways for integrating Artificial Intelligence (AI) into healthcare and identifies their limitations in realising patient-centred care. We propose a fourth pathway: Adaptive Machine Learning (AML). AML strategically integrates AI into learning health systems, allowing continuous model updates using population-level, context-sensitive real-world data. This quintuple aim based approach enhances personalisation, promotes quality and equity, and strengthens system resilience. We identify three critical enablers of AML: integrative data governance, adaptive study designs, and regulatory evidence sandbox facilities. Taken together these elements can advance the goal of sustainable digital health autonomy and responsible, collaborative data use. The aim of this study is to define a practical and ethically grounded framework for operationalising AML as a fourth pathway to patient-centred AI that aligns with international standards for responsible healthcare innovation, equitable governance, and digital transformation. Realising the full potential of AI in patient-centred healthcare requires urgent and coordinated actions across three priority areas to: (1) develop high-priority clinical use cases that demonstrate how AI can safely learn from real-world data and improve patient outcomes; (2) advance adaptive evaluation frameworks that reflect the lived experiences of diverse and underserved populations; and (3) establish regulatory evidence sandboxes to foster transparent, participatory, and multistakeholder innovation. Future research should prioritise integration of collective consent models and alignment of AI and medical device regulations with international governance toolkits to promote safe, patient-centred, inclusive, and trusted AI adoption in health ecosystems…(More)”.