Stefaan Verhulst

Article by Stefaan Verhulst and Andrew Zahuranec: “What would it take for Indigenous communities not simply to be represented in the emerging AI ecosystem but to shape, govern, and benefit from it?

That question is what animated our event during UNGA High-Level Week on Advancing Public Interest AI Through Data Commons for Indigenous Languages and Cultures. 

The discussion brought together Indigenous leaders and technologists, funders, researchers, international organizations, civil society, and industry around a growing challenge: As AI becomes embedded in education, public services, cultural production, and everyday life, many Indigenous communities are being drawn into AI systems that they did not design, using data they may not control, and reflecting assumptions and values that may not be their own.

The stakes are particularly high for Indigenous languages and cultural knowledge. AI could become a powerful instrument for language revitalization, education, cultural transmission, and economic opportunity. But without different approaches to data, infrastructure, governance, and investment, it could just as easily reproduce longstanding patterns of extraction while amplifying misinformation. This leads to weaker foundations of reliable information for AI and other systems that subsequently harm communities…Five themes emerged from the discussion…(More)”.

Paper by Geoff Mulgan and Alex Fischer: “Work in Progress argues that democracy has not failed as an idea, but that many of the ways we practise it remain rooted in the 19th century and have not kept pace with a rapidly changing world. Falling trust, political disengagement and declining participation point to a system that is too often failing to listen, learn and adapt.

Drawing on more than 50 examples of democratic innovation from over 25 countries, Mulgan and Fischer set out a framework for understanding democracy as an interconnected system of functions – from elections and decision-making to public services, scrutiny, knowledge and citizen participation. They explore how these functions can be strengthened, alongside the relational foundations that allow democracy to work effectively.

The paper considers the opportunities and risks presented by digital technology and AI, arguing for deliberate investment in new forms of cognitive infrastructure that can help democracies harness collective intelligence, improve decision-making and rebuild trust.

The authors’ central message is clear: democracy should be treated as a work in progress – something to be upgraded, tested and renewed, rather than a finished achievement to simply defend…(More)”.

Article by Indrabati Lahiri: “Citizen science is rising globally, driven by technology and public participation in research. Volunteers help professional scientists gather critical data across vast geographic areas and timeframes. In tourism, guests at resorts participate in coral planting, wildlife tracking, and environmental DNA collection. Crowdsourced data also tracks climate change, monitors watersheds, and supports disaster preparedness. However, challenges remain regarding data quality, reliability, structural biases, and institutional hesitation. Ultimately, combining volunteer participation with professional expertise helps address data gaps in environmental research…(More)”.

Article by Justin Gest: “Last month, the Trump administration proposed new rules that would limit who is counted in the census. It’s an initiative that is ideologically consistent with President Trump’s voter-suppression efforts, his aggressive deportation policies and his executive order that seeks to purge national parks and museums of information about slavery and Native American history that, in his words, casts our country “in a negative light.”

The Constitution requires the census to count everyone in America, but Mr. Trump clearly wants to decide who counts.

Under current practice, the Census Bureau, which is part of the Department of Commerce, looks to count everyone living here, regardless of citizenship or immigration status. Census data is used for purposes ranging from congressional apportionment to government-funding formulas.

Under the administration’s proposal, the census would count only citizens and green card holders. Undocumented immigrants, international students, asylum seekers, refugees without permanent residency and others would be excluded. The administration argues that individuals in these groups lack the “sufficient tie and allegiance” required to establish them as “true inhabitants” of the United States — a rationale akin to one advanced by some critics of birthright citizenship…(More)”.

Press release by Biohub: “…the U.S. Department of Energy, the National Institutes of Health, and new funding partners today announced a major expansion of an international effort to generate and make accessible the data enabling predictive AI models of biology. Together, the organizations are investing $1.8 billion in funding, data, computation, and new measurement technology, the largest coordinated commitment to generating AI-ready biological data to date. The result will be an open resource for the research community that provides the foundation for greater understanding and ultimately treatment of human diseases.

As part of this announcement, Biohub has partnered with the Department of Energy (DOE) Office of Science and the National Institutes of Health (NIH) to advance the frontier of artificial intelligence in biology. DOE will invest more than $500 million over five years in lab measurement, modeling and computation toward the international effort to build an AI-ready open data resource. NIH will coordinate the contribution of relevant datasets, repositories, and knowledge bases developed through more than $500 million in prior federal investment aligned to this initiative. Biohub will work with NIH to standardize these datasets for AI model training.

In addition, Google DeepMind, Isomorphic Labs, and Meta are collectively investing $300 million in the Virtual Biology Initiative to create the technologies and multi-modal datasets needed to build predictive models of life.

These datasets will enable the global scientific community to collectively build and use AI models that allow researchers to ask, predict, and answer biological questions digitally, accelerating the path to new ways of preventing and treating diseases. This initiative will deliver the foundational measurements to train these models, expanding cell response data to interventions across far more cell types and conditions than have yet been studied, and building and validating technologies for studying cells and cellular interactions at greater scale, speed, and accuracy.

The scale of the challenge explains why no single institution is attempting it alone. Modern AI models in biology, from protein structure prediction to whole-cell simulators, are ultimately limited by the quality and breadth of the experimental data used to train them. Today’s datasets capture cell responses to interventions across only a small fraction of the cell types and conditions that matter in human health. The Virtual Biology Initiative is designed to close that gap by coordinating data generation across institutions and disciplines, expanding cell response measurements to far more cell types and conditions than have yet been studied, and building and validating technologies capable of studying cells and their interactions at greater scale, speed, and accuracy. The result, organizers say, will be a foundational, openly accessible dataset that no laboratory, company, or government agency could produce on its own…(More)”.

Book by Matthew Botvinick: “American democracy is under pressure, as institutions that we once thought stable suddenly seem precarious. At the same time, artificial intelligence has taken over much of our online life, with enormous offline consequences. In AI and Political Freedom, Matthew Botvinick examines the interaction of AI and democracy, exploring how AI is likely to challenge democracy as we move further into the twenty-first century. Botvinick—an expert on both democratic theory and AI technology—finds that the prognosis is serious. AI is poised to destabilize our democratic institutions—to attack them at their most vulnerable points and provide a useful tool for leaders with authoritarian aspirations. Yet despite the potential threat posed by the collision of AI and democracy, Botvinick argues that there are ways that we can safeguard our political freedom.

Botvinick points out that concern about our democratic backsliding emerged just as public attention turned to AI safety. Worries over the proliferation of deepfakes and AI-generated misinformation as well as the expanding political power of technology companies circulated widely—but were seldom connected to concern over the decay of democratic institutions. Botvinick makes this connection, considering AI’s potential effects on our politics. Mass unemployment caused by technological advances creates a receptiveness to authoritarian appeals; security crises could enable governments to abuse emergency powers; and the use of AI in intelligence work creates the risk of politicized surveillance. To stop the retreat from democratic values, Botvinick argues, we must reinforce our democratic institutions and, ultimately, reimagine how democracy works, building richer and more engaging forms of citizen participation…(More)”.

Substack by Stefaan Verhulst: “We live in a paradoxical era. On the one hand, data is growing exponentially, computational modeling and artificial intelligence are advancing at breathtaking speed, and knowledge is deeper and more specialized than ever before. Yet on the other hand, rates of transformative discovery appear to be slowing rather than accelerating across important domains, and crises of replication and generalizability seem to be intensifying. Across the world, research outputs remain stubbornly misaligned with the problems societies—and people—describe as most important to them. In short, we are living through an era of both epistemic abundance and epistemic crisis.

Several explanations are offered for this apparent gap. Misinformation is said to be eroding the foundations of shared knowledge. Cultural polarization is often cited as a barrier to the collaborative inquiry and pursuit of objectivity on which science depends. Outdated incentive structures—publish or perish, funding biases, information overload—are also mentioned. Each of these no doubt plays a role. But an equally important source of the problem lies further upstream, at a stage of knowledge production that receives surprisingly little systematic attention: the questions we ask.

Across sectors and fields, many of society’s most significant shortcomings today stem not from insufficient data or inadequate analytical tools but rather from the framing of inquiry itself. Scientific agendas are shaped by questions that are too narrow and overly method-driven. Policy is constrained by electoral cycles and available metrics. Even the private sector, for all its celebrated dynamism, asks questions bounded by existing business models and short-term returns.

The consequences of such misdirected inquiry are not abstract. It is now well established that women’s health remained systematically underfunded and understudied for decades–not because the tools to investigate it were lacking, but because the questions that shaped biomedical research agendas were framed around male bodies and male norms. Likewise, pandemic preparedness in the pre-COVID era was repeatedly optimized for speed of vaccine development while neglecting questions about distribution, trust, information, and misinformation. In both cases, the answers developed by experts were technically competent. The questions they asked were wrong, or at least incomplete.

All these shortcomings acquire extraordinary new urgency in the age of artificial intelligence. As AI systems become increasingly powerful “answering machines,” capable of generating immediate, plausible-sounding responses to almost any query, the bottleneck in knowledge creation is shifting: from generating answers to formulating the questions worth answering in the first place. The cheaper answers become, the more valuable questions are.

For all the sophistication of our analytical tools, for all the power of our new technologies, and for all our acute and pressing social needs, we still have no science of questions: no systematic infrastructure for studying how questions emerge, how they can be improved, or how they shape the knowledge that societies produce. This essay argues that we need such a science of questions. It traces the intellectual roots of a question-centered approach to knowledge (Section I), diagnoses the institutional failures that perpetuate what we call a “question deficit” (II), examines the politics of who gets to frame inquiry (III), and outlines the key elements of a science of questions (IV)—one made both possible and urgent by the rise of AI…(More)”.

Article by Deepak Bhargava and Felicia Wong: “Liberal democracy is in crisis in the United States, and the roots of that crisis go far deeper than any single politician or political party. Almost 70 percent of Americans do not believe our democracy is working well. Governing elites and the institutions they run have lost credibility. Pervasive and justified anger has led many to embrace dangerous alternatives or to sit out the democratic process altogether. Ours is an anti-system age.

But surprisingly, many policymakers and civil society leaders seem to believe that upcoming elections will allow for a return to “normal,” and enable them to work more or less as they always have. Legislative advocates are now sharpening their campaigns, jockeying over which issues—health care, voting rights, housing, child and elder care, and so on—should take priority. Political and communications consultants are parsing which policies might address voters’ focus on the affordability crisis. Some problems are new, as recognized by experts puzzling out how to rebuild federal agencies decimated by the Department of Government Efficiency (DOGE). But overall, the approach to governance that seeks to make relatively modest change within a broken system has come roaring back. Even recent ideological debates feel like the rehashing of decades-old arguments. We have seen this movie before, and we know how it ends. The gravitational, habitual pull toward familiar ways of working is very strong.

The hard truth is that in the absence of a dramatically different approach to governing, authoritarianism and oligarchy will become even more entrenched, and far harder to uproot. Both have deep roots in the country’s history. These forces will not go away no matter what the next elections bring. Another even more devastating authoritarian breakthrough is the almost predictable result unless future governance makes a break from the past. The stakes are very high…(More)”.

Report by the Federation of American Scientists (FAS): “Data center development has outpaced most local governments’ capacity to respond to it, as evidenced by the increasing amount of moratoria legislation to give time for policy development and conversations with communities.¹ As data center applications ramp, so have requests for policy guidance by local governments. Broadly, we are starting to see nascent best practices, understanding more clearly the knowledge gaps and research needs, and building a community of practice across industries. This report provides new information for local government policy makers, including property value impacts and a robust collection and analysis of community benefit agreements. 

Over the course of 2026, the Federation of American Scientists (FAS) has conducted primary research, including interviews in the field, issued public records requests for difficult-to-find agreements, and created a series of trackers on new ordinances, state legislation, and moratoriums. This report serves as a landscape assessment and toolbox from which local governments can negotiate an informed position when it comes to the levers available to them and includes a first-of-its kind analysis of ten executed agreements between local governments and data center developers.

This report includes three sections:

Ordinances Analysis: provides a brief overview of what local governments are already doing through a landscape scan of 42 local ordinances and 12 state actions.

Data Center Property Values: provides a sampling of data for taxable value over time across six jurisdictions.

Community Benefits Agreements (CBA) – Review and Comparison: provides a comparative review of ten CBAs, identifying common requirements, what is offered as a benefit to the local government and to the developer, and what policy considerations are included within the agreement…(More)”.

Essay by Dennis M. Hogan: “Imagine a large online lecture class taught by a tenured professor with the assistance of AI tutors. The professor writes and delivers her lectures once; they are infinitely re-playable as long as the course content does not change. Meanwhile, AI agents can design assessments, deliver course material beyond lectures, hold students’ hands through the completion of the assignments, and even evaluate students’ work, eliminating the need for human grading. The professor can simply review the AI’s work and enter the grades, though even this is essentially a formality—there’s nothing stopping the AI agent from entering the grades with the registrar directly. What I have just described is a class that costs almost nothing in labor, especially after the professor recorded the lectures in a previous semester. A really ambitious university might assign a single grad student or teaching assistant to the class just to oversee the automated processes, but even this isn’t strictly necessary. The machinery runs on its own.

If you, like me, are attached to the old model of education, which presupposes that teachers, in delivering instruction, enter into personal relationships with students, you might find this scenario depressing. After all, if no one is really teaching, is anyone really learning? If, on the other hand, you are a college administrator desperately trying to align your instructional obligations to your budgetary limitations, you might find such a model enticing. As a student, you might find it alienating, but then again, if you are already used to Zoom school and distance learning, it might not feel all that different. And perhaps the course doesn’t matter to you all that much. You’re just trying to get a requirement or a prerequisite out of the way. This class is not going to change your life. 

Much of the discourse around generative AI in education revolves, fairly or unfairly, around the ways that students can use the technology to do their work for them. AI is frequently connected to what commentators have called a “literacy crisis”—a longstanding decline in Americans’ levels of reading fluency affecting adults as well as children. While some early childhood educators and policy experts are optimistic about the potential for AI tools to improve childhood reading acquisition alongside traditional classroom instruction and reading with adults at home, many college instructors have been more skeptical, suggesting that students who rely on AI tools lack the motivation to absorb written texts and do not build the skills to understand them effectively. But if you listen to its boosters in academia and industry, AI is much more than a cognitive shortcut or a text-generating machine: It is a technology that is rapidly threatening to transform the entire economy, as companies make massive bets on its potential to increase productivity and even solve previously insoluble problems such as cancer and climate change. It could even amount to a new industrial revolution. So far, the promises of AI have been slow to materialize: Although the technology offers applications for tasks like coding, there remains an abiding sense of confusion about whether—and how—to integrate AI products into other lines of work. In fact, among the general public, AI skepticism abounds. This probably has something to do with the fact that AI boosters tell us, repeatedly, that AI is going to put many of us out of work…(More)”.