Stefaan Verhulst
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)”.
Article by Moshe Maor: “This article tackles a fundamental challenge in the study of Policy Innovation Labs (PILs): the absence of a shared, analytically robust definition. Despite their growing prominence as institutional arrangements for addressing complex public problems through experimentation and co-creation, PILs have been defined inconsistently, leading to conceptual ambiguity and blurred boundaries with related initiatives such as living labs or behavioral insight units. The article begins by highlighting the consequences of this ambiguity, including difficulties in comparison, replication, and evaluation of PILs across different contexts. It then outlines a systematic methodology for collecting and analyzing 16 influential scholarly definitions of PILs, identifying recurring dimensions such as innovation orientation, design thinking, experimental approaches, and user-focused engagement. The analysis reveals that while innovation orientation is the most consistently emphasized attribute, other dimensions like user-centeredness and boundary-spanning functions are under-theorized despite their practical importance. Building on these findings, the article proposes a minimal definition of PILs as innovation-oriented entities that employ design-based, experimental, and/or other innovative methods to develop creative responses to complex public problems through systematic user and stakeholder engagement. This definition is designed to provide a clear analytical baseline for cumulative research, distinguishing PILs from adjacent organizational forms while accommodating their contextual diversity. The article also explores the analytical and empirical implications of adopting this definition, including its potential to enhance case selection, typology building, performance evaluation, and theory development in the study of public sector innovation. By clarifying the conceptual boundaries of PILs, this work contributes to a more rigorous and coherent research agenda, ensuring that the term retains its analytical utility and does not become a mere buzzword devoid of meaning…(More)”.
Article by Martin Ho, Pramod P. Khargonekar, Eoin O’Sullivan: “It has been many decades since the American research enterprise operated under the blueprint pioneered by Franklin Delano Roosevelt’s science advisor, Vannevar Bush, in his 1945 report, Science, The Endless Frontier. His postwar model of public agencies steering “Big Science” projects, such as moonshots and particle accelerators, through stable, long-term commitments has given way to a complex ecosystem of public, private, and philanthropic actors with divergent roadmaps, incentives, and risk tolerances. The scientific establishment’s imperative, therefore, is to understand and reconsider how the R&D ecosystem now operates. Above all, can the diverse institutions—federal agencies, universities, industry, and philanthropies—self-organize to achieve ambitious scientific endeavors?
The challenge is particularly acute here in the United States, but it’s not limited to North America. A 2024 review of Horizon Europe, the European Union’s seven-year framework for funding research and innovation, found that “most actors are still in the process of ‘sense-making.’” Shifting from more laissez-faire guidance to a mission-oriented approach, the Horizon programs explicitly tie funding to five societal missions. This kind of grand challenges framework is becoming more popular; since COVID, the United Kingdom committed over £1 billion to its Advanced Research and Invention Agency (ARIA), and Germany devoted €1 billion to its Federal Agency for Breakthrough Innovation (SPRIND)—both high-risk funding agencies that replicate the model of the US Defense Advanced Research Projects Agency (DARPA). New specialist funders, or focused research organizations, have adopted similarly inspired program management practices to de-risk and promote grand challenge–relevant technologies.
Today scientists and engineers face a profound question: How do we tackle grand challenges—climate change, environmental sustainability, energy security, pandemics, cancer, AI governance, and more—when our R&D system is so fragmented? The answer is not to try to restore the old centralized system (which, for all its strengths, often struggled to advance grand challenges), but rather to master new mechanisms that coordinate effectively across a diverse, decentralized network of public agencies, private industry, and philanthropic organizations in ways that make us even more effective than we were before…(More)”.
Blog and Paper by the Royal Statistical Society: “Artificial intelligence is often talked about as if it can think like a person. We hear that it understands, reasons and even creates. But AIs think quite differently to how people think: they are fundamentally statistical. This is a fact that is not widely understood – but I believe that it is an essential point that needs far greater recognition for AIs to be used effectively, safely and ethically.
Large language models (LLMs), the systems behind many chatbots and search tools, are trained on vast amounts of text and data. They look for patterns in that data and use those patterns to predict what is most likely to come next. When they produce an answer, they are not thinking about it in a human sense. They are generating the most likely response based on what they have seen before.
This is what makes them so impressive. It is also why they sometimes go wrong.
Because these systems are statistical, their outputs depend on the data they have been trained on. If that data contains gaps or biases, the results will reflect that. If the system is used in situations that differ from its training data, its performance can change. And even when an answer sounds confident, it is still based on probability rather than certainty. Understanding this helps us use AI more wisely.
It encourages simple but important questions. Where did the data come from? How representative is it? How reliable is the output? How might results differ for different groups of people? What happens when circumstances change?
These questions matter when AI is used to support decisions about jobs, loans, healthcare, education or public services. As AI becomes more common in everyday systems, basic statistical awareness becomes part of digital knowledge.
This is why, led by its AI Task Force, the RSS has published a landmark paper on the statistical nature of AI. Our core argument is clear: AI systems are built on statistical pattern recognition. They need to be developed, evaluated and governed with rigorous statistical precision…(More)”.