Stefaan Verhulst

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)”.

Blog by PUBLIC: “Across the M&E lifecycle, we are already seeing real, deployable applications of AI tools.

• AI-assisted evidence synthesis is probably the most mature area. Tools can now search, screen, and summarise bodies of literature at a scale that would take human teams weeks. For evaluation teams scoping a new programme area, or interested in exploring what some other field could say about their topic, this is genuinely useful today.

A recent example of this is the development of systems like InsightAgent, a multi-agent framework designed for complex systematic reviews. Researchers demonstrated that this tool could partition a massive amount of literature, read and synthesise findings, and draft a rigorous review in just 1.5 hours – a process that traditionally takes months to complete manually. Researchers could also visually monitor the AI’s reading trajectory, adjust its inclusion criteria, and verify its sources in real-time.

• AI-led qualitative interviews – including voice – have been shown to generate substantially richer responses than conventional open text fields. For public sector evaluations, the possibility of running qualitative research at a fraction of the cost is a meaningful shift. Similarly, these practices are effective where there are multiple layers of governance  – such as evaluation framework development and qualitative evaluations of ‘unmonetisable’ outcomes, as per the Green Book.

For example, PUBLIC recently utilised Salomo to conduct user research for a major public sector project. Traditionally, gathering and synthesising user research at this scale would take a team of multiple researchers many months to complete. However, by leveraging Salomo’s agentic capabilities, a team of just two researchers was able to process, code, and extract insights from 100 interviews in less than a week.

• Getting to concrete outputs and models more quickly. Analysis and reporting workflows are starting to allow evaluators to go from a research question to a documented, reproducible output – with code, findings, and visualisations – in a fraction of the time previously required.

For example, AI Scientist-V2, is a system capable of automating the scientific research lifecycle. Given a high-level prompt, the agent autonomously formulates hypotheses, writes and debugs experiment code, visualises data, and drafts a complete manuscript in under 15 hours. It also recently produced a research paper that successfully passed a double-blind peer review.

While public sector policy evaluation has its own unique complexities and stakeholder dynamics, the implication is clear. These are tools that can handle the heavy mechanical execution – running the econometrics, generating charts, and drafting technical annexes – freeing up evaluators to focus on the harder interpretive questions and policy implications…(More)”.

NASCIO Report: “Generative AI helped states draft, summarize and analyze. Now, AI is starting to act.

In this new NASCIO report, we explore the rise of agentic AI — systems that can plan, take limited actions and manage multi-step workflows with human oversight. From routing approvals and detecting anomalies to guiding citizen services from start to finish, agentic AI represents the next phase of AI maturity in state government.
This report helps state technology leaders:

• Understand the difference between generative AI and agentic AI
• Recognize five phases states might go through from generative to agentic AI
• Anticipate governance, security and workforce risks
• Identify practical guardrails and incremental next steps

If your state is exploring agentic AI, this publication is a primer for what is coming next.

Agentic AI is here, and the time to review policies, strengthen guardrails and build trust is now…(More)”.

Article by Mexico News Daily: “Turning to artificial intelligence to keep Mexico’s more than 125,000 missing people from being forgotten, a collective in the state of Jalisco has been crafting “living” videos of the missing that talk to the public.

In the state with the highest number of missing persons, the Luz de Esperanza Collective creates Fichas Vivas de Búsqueda, or Living Search Cards — short AI-generated videos that animate photos and recreate the voices of the disappeared for social media.

The clips circulate online, seeking to cut through the noise and force viewers to confront a national human rights crisis.

Using image, facial animation and speech synthesis tools, families script what their relatives would say and work with technologists to produce videos that resemble digital search posters — with a “photo” of the missing person actually “speaking.”

In one 110-second video, the photo of the missing person declares, “I am Carlos Maximiliano Romera Meza. I was 18 years old when I disappeared, and I want to tell you my story.”..(More)”.

Book by Ben Green: “The field of data science faces a moral crisis. Despite the desires of data scientists to develop algorithms for good, algorithms regularly produce injustice in practice. Given these persistent harms, the field must reflect on difficult questions about its identity and future. Can data science be a force for promoting social justice in the world? What practices should data scientists follow to achieve this goal?

In Algorithmic Realism, Ben Green presents a bold and interdisciplinary approach to data science. Drawing on his experience practicing data science in the public interest, he argues that improving society with algorithms requires transforming data science from a formalist methodology focused on mathematical models into a practical methodology focused on addressing real-world problems. By providing an expanded framework for the “data science workflow”—the steps that characterize the algorithm development process—he offers a practical, step-by-step guide describing how data scientists can apply their skills in service of social justice. Through these contributions, the book reveals a vision for a renewed, but realistic, optimism about data science’s potential to foster a more equitable world…(More)”.

Paper by John Rountree & John Gastil: “Generative artificial intelligence (GAI) interfaces, such as ChatGPT, Bloom, and Gemini, may offer transformative possibilities for deliberative democracy. We argue that deliberation scholars and practitioners should use GAI software to run simulations to complement existing deliberative processes. By simulations, we mean the use of GAI software to run hypothetical deliberations, either with or on behalf of human participants, to support rather than replace human judgment. We expand on the notion of a GAI deliberation simulation and showcase an example using GPT-4o. To illustrate the practical advantages of simulations, we showcase two use cases: training facilitators and providing time-sensitive policy consultation. We also address potential cautions and limitations surrounding GAI simulations, such as concerns about transparency and bias. We conclude by exploring the theoretical implications of GAI simulations for developing and refining models of deliberation dynamics…(More)”.