Stefaan Verhulst

Article by Cory Doctorow: “…The fact that every AI-created work is in the public domain means that if Getty or Disney or Universal or Hearst newspapers use AI to generate works – then anyone else can take those works, copy them, sell them or give them away for nothing. And the only thing those companies hate more than paying creative workers, is having other people take their stuff without permission.

The US Copyright Office’s position means that the only way these companies can get a copyright is to pay humans to do creative work. This is a recipe for centaurhood. If you are a visual artist or writer who uses prompts to come up with ideas or variations, that’s no problem, because the ultimate work comes from you. And if you are a video editor who uses deepfakes to change the eyelines of 200 extras in a crowd scene, then sure, those eyeballs are in the public domain, but the movie stays copyrighted.

But creative workers do not have to rely on the US government to rescue us from AI predators. We can do it ourselves, the way the writers did in their historic writers’ strike. The writers brought the studios to their knees. They did it because they are organized and solidaristic, but also are allowed to do something that virtually no other workers are allowed to do: they can engage in “sectoral bargaining”, whereby all the workers in a sector can negotiate a contract with every employer in the sector.

That has been illegal for most workers since the late 1940s, when the Taft-Hartley Act outlawed it. If we are gonna campaign to get a new law passed in hopes of making more money and having more control over our labor, we should campaign to restore sectoral bargaining, not to expand copyright…(More)”.

Book by Ali Cheshmehzangi: “This book presents a bold reimagining of urban futures through the convergence of generative artificial intelligence and digital twin technologies. This book presents AI-powered digital twins as catalysts for systemic change, civic empowerment, and environmental regeneration rather than just as planning tools at a time when cities are dealing with growing climate stresses, infrastructure stress, and profound social inequality. This book explores the development of urban digital twins from data-driven models to intelligent, adaptive systems that learn, simulate, and co-design with their urban settings. It does this via eleven technically sound and conceptually rich chapters. It investigates how generative AI may improve climate simulation, manage floods, lessen urban heat, lower emissions, and promote participatory planning, all while posing important ethical, equitable, and governance issues. This book covers open data standards, AI-twin integration architectures, and the difficulties of implementing prototypes into citywide systems, moving from fundamental theory to state-of-the-art practice. It demonstrates how these technologies can be used to depict community-driven urban scenarios, model circular material flows, and build green roofs. Throughout, this book maintains that cities’ ability to restore ecosystems, incorporate a variety of viewpoints, and envision resilient and just futures are what truly define intelligence, not efficiency alone. This book promotes a new urban paradigm where ethics are ingrained, intelligence is dispersed, and regeneration becomes the design axiom. It does this while keeping a close eye on both potential and responsibility. This book provides scholars, planners, technologists, and policymakers with a visionary yet doable road map for creating cities that are not just intelligent—but profoundly alive—by drawing on real-world examples, speculative design theory, and systems thinking. This is not a book about managing cities more efficiently. It is a book about reconsidering the basic concept of urban intelligence and co-creating the urban futures filled with care, courage, and collective imagination…(More)”.

Book by William Rankin: “Maps are ubiquitous in contemporary life­­—not just for navigation, but for making sense of our society, our environment, and even ourselves. In an instant, huge datasets can be plotted on command and we can explore faraway places in exacting detail. Yet the new ease and speed of data mapping can often lead to the same results as ever: over-simplified maps used as tools for top-down control.

Cartographer and historian William Rankin argues that it’s time to reimagine what a map can be and how it can be used. Maps are not neutral visualizations of facts. They are innately political, defining how the world is divided, what becomes visible and what stays hidden, and whose voices are heard. What matters isn’t just the topics or the data, but how maps make arguments about how the world works. And the consequences are enormous. A map’s visual argument can change how cities are designed and how rivers flow, how wars are fought and how land claims are settled, how children learn about race and how colonialism becomes a habit of mind. Maps don’t just show us information—they help construct our world.

Brimming with vibrant maps, including many “radical” maps created by Rankin himself and by other cutting-edge mapmakers, Radical Cartography exposes the consequences of how maps represent boundaries, layers, people, projections, color, scale, and time. Challenging the map as a tool of the status quo, Rankin empowers readers to embrace three unexpected values for the future of cartography: uncertainty, multiplicity, and subjectivity. Changing the tools—changing the maps—can change the questions we ask, the answers we accept, and the world we build…(More)”.

Article by Cosima Lenz, Stefaan Verhulst, and Roshni Singh: “Women’s health has long been constrained not simply by a lack of research or investment, but by the absence of a clear, collectively defined set of priorities. Across the field, the most urgent questions – those that reflect women’s lived experiences, diverse needs and evolving health challenges – are too often unidentified, under-articulated or overshadowed by legacy agendas. Consequently, decision-makers struggle to allocate resources effectively, researchers work without a shared compass and innovation efforts risk overlooking the areas of greatest impact.

That’s why identifying and prioritising the questions that matter is essential. Questions shape what gets studied, what gets measured and whose needs are addressed. Yet historically, these questions have been selected in fragmented or opaque ways, driven by the interests of a limited set of stakeholders. This has left major evidence gaps, particularly in areas that disproportionately affect women, and has perpetuated inconsistencies in diagnosis, treatment and care…

The GovLab’s 100 Questions Initiative is such a global, multi-phase process that seeks to collectively identify and prioritise the ten most consequential questions in a given field. In partnership with CEPS, and with support from the Gates-funded Research & Innovation (R&I) project, this methodology was applied to women’s health innovation to define the Top 10 Questions guiding future research and innovation.

The process combined topic mapping with experts across research, policy, technology and advocacy, followed by collecting and refining candidate questions to address gaps in evidence, practice and lived experience. More than 70 global domain and data experts contributed, followed by a public voting phase that prioritised questions seen as both urgent and actionable. The full methodology is detailed in a pre-publication report on SSRN.

The Top 10 Questions are diagnostic tools, revealing evidence gaps, system failures and persistent assumptions shaping women’s health. By asking better questions, the initiative creates the conditions for more relevant research and improved outcomes…(More)”.

European Parliament: “In the context of the wars in Ukraine and other parts of the world, the increasingly global effects – material and political – of war make it more important than ever to measure the level of threats to peace, security and democracy around the world. The Normandy Index has presented an annual measurement of these threats since the 2019 Normandy Peace Forum. The results of the 2025 exercise suggest the level of threats to peace is at its highest since the index was launched, confirming declining trends in global security resulting from conflict, geopolitical rivalry, growing militarisation and hybrid threats. The findings of the 2025 exercise draw on data compiled in 2024 and 2025 to compare peace – defined on the basis of a given country’s performance against a range of predetermined threats – across countries and regions. Derived from the Index, 63 individual country case studies provide a picture of the state of peace in the world today. Designed and prepared by the European Parliamentary Research Service (EPRS), in conjunction with, and on the basis of data provided by, the Institute for Economics and Peace, the Normandy Index is produced in partnership with the Region of Normandy. The paper forms part of the EPRS contribution to the 2026 Normandy World Peace Forum…(More)”.

Book edited by Johanna Seibt, Raul Hakli and Marco Nørskov: “Robophilosophy is the philosophical engagement with the phenomena and problems that arise with “social robots”: robots developed for use everywhere in society, at work, in public spaces, or at home. This new area of research is applied philosophy undertaken in close contact with, or even as part of, empirical research in the multidisciplinary areas of human–robot interaction studies and social robotics. It includes, but goes beyond, ethical considerations, offering new research in social ontology, philosophy of mind, metaphysics, and more.

The book explores the wide-ranging questions we currently have about the new class of artificial social agents generated by robotics technology. Written by researchers from philosophy, psychology, and the technical sciences, the book shows how philosophical knowledge can help us to navigate the unprecedented sociocultural risks arising from this technology…(More)”.

Paper by Stefaan Verhulst: “As societal challenges grow more complex, access to data for public interest use is paradoxically becoming more constrained. This emerging data winter is not simply a matter of scarcity, but of shrinking legitimate and trusted pathways for responsible data reuse. Concerns over misuse, regulatory uncertainty, and the competitive race to train AI systems have concentrated data access among a few actors while raising costs and inhibiting collaboration. Prevailing data governance models, focused on compliance, risk management, and internal control, are necessary but insufficient. They often result in data that is technically available yet practically inaccessible, legally shareable yet institutionally unusable, or socially illegitimate to deploy. This paper proposes strategic data stewardship as a complementary institutional function designed to systematically, sustainably, and responsibly activate data for public value. Unlike traditional stewardship, which tends to be inwardlooking, strategic data stewardship focuses on enabling cross sector reuse, reducing missed opportunities, and building durable, ecosystem-level collaboration. It outlines core principles, functions, and competencies, and introduces a practical Data Stewardship Canvas to support adoption across contexts such as data collaboratives, data spaces, and data commons. Strategic data stewardship, the paper argues, is essential in the age of AI: it translates governance principles into practice, builds trust across data ecosystems, and ensures that data are not only governed, but meaningfully mobilized to serve society…(More)”.

Paper by Stefaan Verhulst, Cosima Lenz, Roshni Singh, Marta Dellaquilla and Leonie Kunze: “Women’s health remains under-resourced, underprioritized, and narrowly defined. Across the life course, women experience distinct health needs with significant implications for health and wellbeing, yet persistent gaps in evidence and data continue to reinforce inequities. In the absence of a universally accepted definition of women’s health, this study aimed to develop a topic map to capture its breadth and to identify an evidence-informed set of the top ten priority questions to guide future women’s health research and innovation. We used a participatory, iterative methodology inspired by the 100 Questions Initiative, combining structured stakeholder engagement, rapid evidence synthesis, and iterative validation. An initial topic map was developed through an in-person workshop and refined through ongoing engagement with 77 global experts in women’s health and data science. Guided by the topic map, experts submitted research questions via a virtual survey, which were refined, clustered, prioritized, and ranked. The topic map served as a shared framework to guide the submission of actionable research questions and comprised four branches: (1) key domains of women’s health; (2) determinants and barriers; (3) technology and innovation; and (4) research and evidence gaps. A total of 113 questions were submitted, clustered into 56 themes, and narrowed to a top ten through expert prioritization, followed by public ranking. The highest-ranked questions focused on reframing and prioritizing women’s health, strengthening investment and innovation ecosystems, and addressing evidence gaps, research participation, data quality, and equity. This study presents a comprehensive topic map that captures the complexity of women’s health and provides a unifying framework for the field. The prioritized questions offer a strategic foundation to guide future research, policy, and investment to advance women’s health innovation…(More)”.

Report by Claudia Chwalisz and Sammy McKinney: “Citizens’ assemblies and other democratic innovations are spreading around the world. But they do not spread by themselves. Behind every successful scaling story sits a constellation of organisations doing the essential, often invisible work of building capacity, establishing networks, advocating with decision makers, and ensuring quality standards.

These are what we call scaling catalysts: organisations that intentionally drive the expansion of democratic innovations in their regions.

In this paper, we make three core contributions to the field:

1. We distil six features of effective scaling catalyst organisations, aiming to elevate the important role they play.

2. We examine critical tensions and trade-offs these organisations face, and how they can navigate these.

3. We identify five frontiers of future practice that can further accelerate the scaling of democratic innovations and promote more deliberative cultures beyond the work of individual catalyst organisations.

This paper is for three key audiences: We offer insights for practitioners building similar organisations, for funders seeking to support this vital work, and for researchers identifying knowledge gaps…(More)”

Article by Cade Metz: “For decades, elite mathematicians have struggled to solve a collection of thorny problems posed by a 20th-century academic named Paul Erdos.

This month, an artificial intelligence start-up called Harmonic jumped into the mix. Harmonic said its A.I. technology, Aristotle, had solved an “Erdos problem” with help from a collaborator: OpenAI’s latest technology, GPT-5.2 Pro.

For many computer scientists and mathematicians, solving an Erdos problem showed that artificial intelligence had reached a point where it was capable of doing legitimate academic research. But some experts were quick to point out that the solution generated by A.I. was not very different from earlier work done by human mathematicians.

“It feels to me like a really clever student who has memorized everything for the test but doesn’t have a deep understanding of the concept,” said Terence Tao, a professor at the University of California, Los Angeles, who is regarded by many as the finest mathematician of his generation. “It has so much background knowledge that it can fake actual understanding.”

The debate over what Harmonic’s system accomplished was a reminder of two consistent questions about the head-spinning progress of the tech industry’s A.I. development: Did the A.I. system truly do something brilliant? Or did it merely repeat something that had already been created by brilliant humans?

The answers to those questions could provide a better understanding of the ways A.I. could transform science and other fields. Whether A.I. is generating new ideas or not — and whether it may one day do better work than human researchers — it is already becoming a powerful tool when placed in the hands of smart and experienced scientists.

These systems can analyze and store far more information than the human brain, and can deliver information that experts have never seen or have long forgotten.

Dr. Derya Unutmaz, a professor at the Jackson Laboratory, a biomedical research institution, said the latest A.I. systems had reached the point where they would suggest a hypothesis or an experiment that he and his colleagues had not previously considered.

“That is not a discovery. It is a proposal. But it lets you narrow down where you should focus,” said Dr. Unutmaz, whose research focuses on cancer and chronic diseases. “It allows you to do five experiments rather than 50. That has a profound, accelerating effect.”

The excitement around GPT-5’s math skills began in October when Kevin Weil, vice president of science at OpenAI, said on social media that the start-up’s technology had answered several of the mind-bending Erdos problems.

Designed as a way of measuring mathematical ingenuity, the Erdos problems are elaborate conjectures or questions that test the limits of the field. The aim is to prove whether each is right or wrong.

Some problems are enormously difficult to solve, while others are easier. One of the more famous problems asks: If the integer n is greater than or equal to 2, can 4/n be written as the sum of three positive fractions? In other words, is there a solution to 4/n=1/x+1/y+1/z?

That problem is still unsolved. But on social media, Mr. Weil boasted that GPT-5 had cracked many others. “GPT-5 just found solutions to 10 (!) previously unsolved Erdos problems, and made progress on 11 others,” Mr. Weil wrote. “These have all been open for decades.”

Mathematicians and A.I. researchers quickly pointed out that the system had identified existing solutions buried in decades of research papers and textbooks. The OpenAI executive deleted his social media post. But even if the initial excitement was overstated, the technology had proved its worth…(More)”.