Stefaan Verhulst
Paper by Marco Marini, Jim Tebrake, and Andinet WoldemichaelThis paper presents a methodology for downscaling official national and subnational macroeconomic data into high-resolution grids using spatial machine learning techniques. Traditional macroeconomic data are highly aggregated and obscure the spatial distributions needed to understand and quantify local economic activity, impacts of physical risks, and infrastructure gaps. Our hierarchical approach integrates official macroeconomic accounts with Earth observation predictors, such as nighttime lights, built-up areas, land cover, and gridded population, to deliver high-resolution, gridded estimates that remain fully consistent with official subnational and national totals. The empirical application focuses on constructing experimental gridded GDP by ten-sector industry for Canada and the United States. The methodology complements and enhances official statistics by adding spatial granularity through open geospatial datasets and can be extended to generate gridded capital stock estimates…(More)”.
Initiative by the University of Chicago: “Papers have been the de facto unit of knowledge in science. But science is not static and a paper is not meant to be the final destination, especially in empirical research. A paper may propose a novel way to look at the world and offer a snapshot of what it finds. That snapshot is far from the whole story.
The world keeps changing after a paper is published. New data, new models, and new methods give us a chance to return to its questions: does the pattern persist, does the effect size remain the same, and what has changed? As AI agents become more capable, we have an opportunity to make revisiting important work a routine part of science.
Today, we are introducing Living Science, a project to keep influential research alive by revisiting its findings as new data, models, and methods become available. We are starting with economics, focusing on studies whose data sources are regularly updated. The goal is to not assess whether the paper was correct at the time of publishing but to provide resources and venues for continued discussions of influential research.
We use SAI’s replication agent to attempt to reproduce each paper’s key findings, documenting what matches and what does not, before extending the analysis to newer data where possible. The AI agent will first look for public replication packages if available and then identify relevant data sources for extending the analysis. As a result, we may not use exactly the same dataset and setup. The exercise is closer to writing a follow-up paper than strict replication. We sanity-check the results and reach out to the original authors for feedback. We also invite the community to identify issues and post comments, helping us improve the analyses over time…(More)”.
Report by the WEF: “As AI, clean energy, advanced manufacturing and infrastructure renewal accelerate, there is strong demand for the practical, hands-on skills needed to build, operate, maintain, repair and produce the goods and systems that economies depend on.
This third instalment of the New Economy Skills series examines how these maker skills are becoming a strategic advantage for economies and businesses. Drawing on unique data and insights, the report analyses global supply and demand trends. It finds that the maker skills pipeline is not yet prepared for the scale, pace or complexity of current and future demand. The report outlines how to address three key challenges: adapting to changing skill requirements, attracting more people into maker careers, and ensuring that maker skills are easier to assess, recognize and credential…(More)”.
Essay by Alondra Nelson: “…argues that while generative artificial intelligence (AI) can be described as a platform, that description is analytically insufficient for understanding where power operates and how it displaces democratic accountability. The platform concept directs attention to the interface—application programming interfaces (APIs), app stores, and developer ecosystems—while the decisive conditions of generative AI lie upstream in compute supply chains, mineral extraction, semiconductor chokepoints, energy and water infrastructures, immigration flows, and corporate-state arrangements that concentrate control. To name this formation, I introduce the concept of structured absence: the organized diminution of democratic institutional capacity in which governance persists while public authorization disappears. Generative AI is not ungoverned; it is extensively governed through export controls, industrial policy, labor regimes, and private infrastructures that remain largely inaccessible to democratic publics. I argue that “predatory inclusion” and authorization are central analytic terms for understanding this shift: communities are incorporated into AI systems on unequal and opaque terms, while the standing to deliberate over those systems is withheld. The essay concludes by calling for democratic infrastructures capable of authorizing technological change…(More)”.
Paper by Sille Sepp; Massimiliano Claps; David Regeczi; F. Manlio Bacco; Olga Batura; Sara Thabit Gonzalez; and Diletta Di Marco: “Sovereign and trustworthy data sharing is critical for addressing local and regional challenges that communities face, as well as foster economic development in European
communities. The technical, business, and governance enablers emerging around Common European Data Spaces provide support to federated data sharing.
Various axes of evolution for federating data in local communities exist: from sharing data within the local administration to extending data space participation to more and different types of stakeholders, and expanding use cases across various domains.
Stakeholders’ willingness to federate may vary, depending on how a data space is designed and implemented. The role play, co-organised by JRC and DSSC and carried out in March 2026, highlighted that stakeholders are more likely to federate if the value proposition is clearly defined and resonates with their objectives. Also, private sector orchestration was considered effective in the case of clearly bounded initiatives.
Advancing federated data sharing requires further efforts to translate abstract principles, such as sovereignty, into practice, address complex incentives and barriers for data cooperation, understand effective drivers for data sharing capabilities, and foster sufficient balance between market demand and supply…(More)”.
Paper by Valerie Sticher et al: “With armed conflicts at a historic high and attacks on civilians rising, understanding the evolving nature of conflict is a critical research priority. The current paradigm in conflict research relies heavily on text-based data, using fatalities as the primary—and often sole—proxy for violence intensity. Although these data have expanded our ability to study armed conflict, they exhibit inherent limitations due to uneven human reporting. War damage assessments based on satellite data offer a complementary perspective. Satellite-derived data have their own limitations, but these arise from different mechanisms, creating distinct, complementary strengths that can be leveraged through data integration. Here we propose three concrete approaches to integration: improvement, enrichment and fusion. Each bridges a different gap in the underlying data sources. We use case studies from Ukraine and Myanmar to illustrate how integration can be implemented in practice and the novel analytical insights that emerge. Prioritizing data integration enables a paradigm shift away from fatality-centric research towards a broader spectrum of violence, revealing the complexity of conflict dynamics…(More)”
Article by Jacob Taylor, Scott E. Page, Kershlin Krishna, Adam Aley, Vivek Ramakrishnan, Sarah Mills, Joshua Becker, Eirini Malliaraki, Fahd Husain, and Sanjay Purohit: “As the artificial intelligence (AI) boom brings large data center proposals to small and rural communities across the United States and beyond, the most resounding story to emerge is one of mismatched agency. In journalist Jasmine Sun’s words, “big companies descend on a small town and run roughshod over small-d democracy.”
Many people do not want data centers in their communities. And for good reasons: the costs are material, if still not fully understood, spanning noise and visual pollution, land-use change, and rising electricity demand with knock-on effects for water withdrawals and utility affordability. As with most large-scale developments, data centers also offer economic upsides—a surge of construction jobs, considerable tax revenue, and improvements in infrastructure. These can be significant but are not always guaranteed.
Given the scale and impact of data centers, people should have a voice. However, decisionmaking about data center siting is often fragmented, noninclusive, and based on criteria other than their impact on communities. In many parts of the country, states set tax exemptions and approve utility contracts, while local officials, often working part-time, negotiate one siting approval at a time, up against experienced corporate counsel imposing time pressure and confidentiality agreements. Local citizens are rarely directly involved in these processes and, where confidentiality provisions apply, may learn the terms of a proposed deal only after negotiations are well advanced. The result is that residents feel sidelined, fueling skepticism as to whether data center proponents are sharing all relevant information and whether public officials are acting in the community’s best interests. Because data center developments are closely associated with the rapid diffusion of generative AI in the U.S. and elsewhere, uncertainty about the projects themselves can also become entangled with broader concerns about AI’s economic and social impacts, as well as its existential risks.
Known remedies for these challenges exist—namely, community-led processes that surface varied priorities, align on a common vision, and help turn it into action. Historically, such processes have proven costly and complex, requiring time to build the trusting relationships on which collaboration depends—time that can be particularly scarce when communities are responding to fast-moving development proposals. Deliberative processes become more difficult to run when issues are emotionally and politically charged, as is increasingly the case with data center developments in the U.S…(More)”.
Announcement by The GovLab: “Questions shape what becomes visible, what is investigated, what is funded, and ultimately what societies come to know. They are fundamental not only to scientific inquiry but to a wide range of sectors and practices—from education and journalism to policymaking, medicine, law, and business, among others. Despite this foundational role, questions are seldom treated as objects of systematic study. Established fields examine intelligence, knowledge production, and decision-making, yet few, if any, take questions themselves as their subject.
Today, questions are most often discussed in narrow, instrumental terms: as prompts to feed an AI system or as items on a survey. Beyond these framings, we tend to assume that we are all naturally competent questioners, drawing on the innate urge to ask that emerges in early childhood and drives how we learn. Yet the capacity to ask does not guarantee the capacity to ask well.
How should we define a good question? Which questions are most likely to serve as stepping stones toward breakthrough insights? What kinds of questions inform different types of decisions? Who determines which questions matter most? How do we architect a line of inquiry: that is, how does one question productively lead to the next? How are curiosity and questioning related? Which questions endure across generations, and which quietly fade?
What, in short, do we actually know about questions?
At present, we lack a science of questions: a field dedicated to understanding how questions emerge, how they evolve, which questions make the greatest impact, and how they shape the trajectory of knowledge across disciplines and generations…(More)”.
Blog by Adam Zable, Stefaan Verhulst, and Sruthi Raghavan: “Data governance has become foundational to how organizations create public value, manage risk, and deploy AI responsibly. As data becomes embedded across more organizational functions – and as AI expands how data can be combined, analyzed, and acted upon – the question is no longer whether organizations need data governance, but whether their governance arrangements are fit for purpose.
Are responsibilities and decision rights clear? Do the right people have the authority and capabilities they need? Can data be accessed, shared, and reused responsibly? Are governance arrangements aligned with organizational priorities and the needs of the people affected by data use?
Answering these questions is harder than it sounds.
To help organizations do so systematically, we developed the Data Governance Self Assessment, an interactive tool that helps organizations examine the maturity of their data governance practices, identify gaps, and set priorities for action. It translates the UNESCO Data Governance Toolkit: Navigating Data in the Digital Age into an operational diagnostic that organizations can use in their own context…The Assessment can be completed by an individual, but its value can be even greater when used collectively. Senior leaders, data stewards, legal teams, technologists, policy teams, and program managers may have very different understandings of how data is governed within the same organization. Those differences are themselves useful information. Comparing perspectives can reveal responsibilities that are unclear, practices that exist but are not widely understood, institutional bottlenecks, or areas where formal principles are not yet translated into day-to-day practice.
Used in this way, the Assessment can offer more than a simple diagnostic. It can also provide a starting point for dialogue about where an organization is today, what matters most, and what should happen next.
The Assessment forms part of a broader ecosystem of guidance, capacity development and multistakeholder collaboration that aims to help translate responsible data governance principles into context-appropriate action in the digital and AI era.
Take the Data Governance Self Assessment: https://datagov.opendatapolicylab.org/..(More)”
Article by Elie Dolgin: “This kind of AI-assisted brainstorming is becoming increasingly common. Researchers have already used Co-Scientist, developed by Google in Mountain View, California, to find a drug combination that kills leukaemia cells in a dish and identify a treatment that, in the lab, regenerates liver tissue damaged by disease.
And Co-Scientist isn’t the only game in town. Frontier AI labs, including Anthropic and OpenAI, and start-ups such as FutureHouse and Phylo (all four based in or around San Francisco, California) are rolling out systems that can tackle tasks once reserved for human scientists, freeing researchers to focus on the most consequential questions and decisions. “We imagine it to be like a collaborator — a partner with you,” says Vivek Natarajan, an AI researcher at Google, who helped to develop Co-Scientist.
That could change not just how scientists work, but also how they are valued. For generations, scientific progress has depended on researchers who could pose difficult questions, devise ways to answer them and make sense of the results. As machines take over more of that work, the scarce resource could end up being human scientific judgement: knowing which questions are worth asking and which lines of enquiry worth pursuing. “The most valuable part now is actually asking the question,” says Ajay Agrawal, an economist at the University of Toronto Rotman School of Management in Canada, who studies AI’s effects on innovation and entrepreneurship…(More)”