Stefaan Verhulst

Book edited by Christophe Geiger and Bernd Justin Jütte: “Access to knowledge and information is essential to foster innovation. In the EU, existing copyright rules pose significant barriers to research and education. Instead of promoting access to knowledge resources, copyright creates legal uncertainty for researchers and educators and enables information intermediaries to exercise strict control over the use of protected works. This edited volume proposes ways out of the copyright conundrum by rethinking copyright as an access right…(More)”.

Article by Stefaan Verhulst and Cosima Lenz: “…Significant knowledge gaps remain regarding conditions that disproportionately affect women, sex-specific differences in disease presentation and progression, and the ways in which health systems respond to women’s needs across the life course. These gaps have tangible consequences. Clinical guidelines, diagnostic pathways, health technologies, and policy decisions are frequently informed by evidence that inadequately accounts for sex and gender differences, contributing to delayed diagnoses, poorer health outcomes, and persistent inequalities in care.

Fortunately, momentum is building to address this imbalance. On July 15, the American College of Obstetricians and Gynecologists (ACOG), the Society for Women’s Health Research, and the Women First Research Coalition released a National Strategy to Close the Women’s Health Gap, calling for a “women’s health moonshot” comprised of $20 billion in federal research investment over ten years. It was subsequently endorsed by dozens of organizations. This ambition is welcome and overdue. And it complements the various new strategies, investment initiatives and advocacy efforts that have emerged across Europe and globally.

But all these proposals for more funding also raise a fundamental question: if substantially more resources become available for women’s health, how should we decide where they should go? For instance, the US strategy calls for various pathways to increase funds for research and evidence but doesn’t prioritize specific areas that could be transformative if funded and studied more.

Closing the women’s health gap is therefore not only a funding challenge. It is also an agenda-setting challenge; and a questions gap. Investment decisions inevitably reflect assumptions about what counts as women’s health, which gaps matter most, what evidence is needed, and whose priorities should shape the research agenda. Getting those questions right is essential if new investment is to address historically neglected needs rather than reinforce existing patterns of attention and funding.

Women’s health encompasses an extraordinarily broad range of issues spanning biological, social, economic, technological, and environmental dimensions. In a context of finite resources, priority-setting becomes essential. Determining which questions matter most is not simply a technical exercise; it is a strategic process that shapes the future direction of research, policy, and investment. Yet, we lack sufficiently systematic and inclusive mechanisms for determining which unanswered questions matter most, to whom, and for what purpose…(More)”.

Blog by Beth Noveck: “…As The GovLab’s Open Data Policy Lab notes(opens in new window): open and accessible government data can “improve the quality of the generative AI output but also help expand generative AI use cases and democratize access to open data.” 

By opening up their own data(opens in new window), governments can build new and better services for all of us.  In Indiana, for example, the Indiana.gov(opens in new window) assistant sits on top of agency documents and databases. Instead of sending residents spelunking through PDFs, it answers questions in plain language and points to the right form or program, thanks to the underlying information that has been cleaned up and exposed in ways a model can use.

South Korea’s AI Hub has already provided millions of records to train applications like TTCare(opens in new window), a mobile application to analyze eye and skin disease symptoms in pets. The app’s AI model was trained on roughly one million pieces of data—half of which came from the South Korean government’s AI Hub. 

In Abu Dhabi, Bayaan(opens in new window) ingests official statistics and lets policymakers pose natural-language questions—“How did youth unemployment change after 2022?”—and get back charts and citations that trace every claim to the source. The output is useful because it’s accountable to public data.

With secure access to administrative data from the UK Biobank and validated against Denmark’s national health records, European researchers built the Delphi-2M Model(opens in new window). Delphi doesn’t just predict whether someone might get cancer or diabetes; it can simulate the course of more than a thousand diseases over a lifetime. That kind of leap is only possible because governments invested in collecting, standardizing, and securely sharing their data…(More)”.

Report by Anastasija Nikiforova, Paula Rodriguez Müller, And Luca Tangi: “Proactive public services (PPS) – that is, services that are initiated by public administrations without a prior request from citizens or businesses – are increasingly discussed as a way to reduce administrative burden, prevent non-take-up and improve access and equity across the EU. The study documented in this report shows that the real challenge is not whether such services are technically possible, but whether administrations are sufficiently prepared to pursue them in a lawful, legitimate and sustainable manner.
Drawing on a systematic literature review, 20 expert interviews across 12 EU Member States and two consolidation workshops, the study finds that progress towards PPS remains uneven across the EU, mostly due to legal clarity, organisational capacity, interoperability, accountability and public trust all often lagging behind the technical capability. While AI is widely discussed as a key enabler of PPS, mature use cases remain rare. Responsible AI considerations are often addressed only implicitly or reactively in practice, suggesting that there is a gap between policy ambitions and implementation realities.
To support analysis and implementation, the report develops a PPS readiness framework and a five-level maturity model that support self-assessment, identify bottlenecks to scaling and highlight readiness gaps across key governance dimensions. The study concludes that PPS should be treated as a governance and service design choice, not as a universal technological end point, and that EU and Member State action should focus on strengthening readiness, providing context-sensitive support, improving legal and operational clarity and fostering trust in proactive service delivery…(More)”.

Book Review by Tim Christiaens of The Rulers: Corporate Power in the Age of AI and the Cloud. Cecilia Rikap. Verso. 2026: “…Big Tech corporations and their products have infiltrated nearly every aspect of our everyday lives, from Apple smartphones to Amazon grocery deliveries and from OpenAI’s ChatGPT (and its many competitors) to Meta’s recently launched smart glasses. The pace at which this infiltration is happening is accelerating: a handful of American tech firms have colonised our lifeworld and there is almost no island of refuge left.

Most recent books on AI and Big Tech domination explore how Silicon Valley dominates workers, consumers, and/or governments. Exemplary publications by, among others, Kate Crawford, James Muldoon, Shoshana Zuboff, and Cathy O’Neil have built the genre. But in The Rulers: Corporate Power in the Age of AI and the Cloud, Cecilia Rikap shows the type of corporate domination underlying these news stories. A crucial gap about AI and Big Tech is how tech corporations exert control over other businesses to expand their corporate empire. We still often think about “big corporations” through the lens of Fordist monopoly capitalism, when businesses would dominate markets through vertical integration. One company then tries to own and control all elements in its supply chain. Such businesses grow by reinvesting profits in their own self-expansion, by acquiring competing smaller firms, and by managing internal research labs to establish R&D that keep competitors out of the race.

The rise of intellectual monopolies

Today’s networked capitalism, however, no longer favours vertically integrated firms. It prefers more nimble companies that coordinate an assemblage of subordinate yet nominally independent organisations. Big Tech establishes its monopolistic hold over everyday life not by growing one single corporation, but by managing an extended universe of supposedly autonomous organisations. Just like you might watch different Marvel movies which are all in fact the same Disney-owned intellectual property, you can have seemingly separate interactions with Google, YouTube, an AI ethics department at an American Ivy League University and your local municipal government, without knowing that these are all different faces of the same Alphabet extended universe…(More)”.

(Open Access) Book by Andreas T Hirblinger: “This book explores how we can make sense of the increasing use of digital technology in peacebuilding. It studies digital peacebuilding at different stages of conflict, including conflict prevention, conflict mitigation, peace mediation, and sustainable transitions from armed conflict. Moving beyond a focus on individual tools, the book encourages a ‘post-digital’ posture that enables a reflexive position vis-à-vis the appeal of digital innovation. It suggests exploring how technologies are always socially embedded, and how the socio-technicality of digital peacebuilding conditions prospects of conflict transformation. To this end, the book develops the concept of apomediated peacebuilding, which suggests that authoritative knowledge about conflict and peace that underpins digital peacebuilding is generated in decentred peer-to-peer networks that involve both humans and machines. It also encourages a critical-reflexive engagement with how claims about technology and society shape digital peacebuilding. In addition, the book is interested in how these dynamics and outcomes differ globally depending on variations in the degree of digitalization of the conflict context and levels of political repression. The book presents comparative empirical research on digital peacebuilding initiatives in South Sudan, Sri Lanka, and Northern Ireland, as well as illustrative examples from several other cases. It delves into a variety of applications, including social media monitoring to counter harmful speech, the use of mobile phones and mobile apps to enable localized early warning and early response, the integration of artificial intelligence (AI) in digital dialogues to support peace processes, and everyday online interactions that can foster societal reconciliation and political change…(More)”.

Blog by Tajh L. Taylor: “The public discourse about new data center construction has reached a fever pitch. Local and state governments are enacting moratoria on new data center construction, because the political reality is that citizens have decided they don’t want to endure escalating energy prices and water consumption for the benefit of a few massive corporations datacenter bans. AI companies themselves are going into so much debt to build out computing infrastructure that it is possibly affecting 30 year Treasury yields, which are at their highest point since 2001. As is always the case, when public discourse gets this polarized, the sensible path forward becomes harder to see.

I feel the need to keep repeating this: not all AI needs massive amounts of water or power, nor does it even necessarily need to be hosted in massive data centers powered by highly polluting generators futurism. Lots of good and useful AI applications are being deployed that run on more ordinary-scaled hardware, or even small devices “at the edge” – meaning in your office, on the factory floor, in your home, on your phone. The application areas are varied and include medical diagnosis, voice assistive technology, sorting produce, fraud detection, et cetera. Many of these application areas predate the current generative AI boom, but have benefited from the substantial investments into AI research and product development.

Moreover, not all AI labs working on gen AI are working on hyperscaled intelligence. Some of the labs working on the frontier of small and efficent AI:

• Rootcomputer builds and studies models with 7B or fewer parameters. That’s small enough to run on many consumer devices such as phones and laptops.
• Sakana.ai is doing research in a few different directions, including on efficiency of key-value memory in training while keeping compute efficiency low.
• Liquid.ai works on device-native foundation models aimed at deployment on phones, laptops, cars, and other embedded hardware.

Some of the bigger and more well-known companies also work on this:

• Qwen is Alibaba’s AI lab, and they have produced a series of increasingly powerful small models that have taken almost all the recent attention in the AI self hosting world.
• Apple ML Research works on intelligence specifically constrained to (Apple) phone/laptop hardware

It’s not a coincidence that you don’t hear or see much about this work. The loudest voices in this conversation aren’t interested in these paths, and they aren’t doing themselves any favors in persuading anyone to take their positions who isn’t already aligned. Like so much discourse these days, it is more of a sorting exercise than a discussion to develop thoughts and change minds.

Here’s what will break the logjam: as the frontier of the most powerful AI continues to advance, fewer use cases need frontier capability. And as companies deploying AI have learned in the past few months, it isn’t a smart use of budgets to max out token spend on the most expensive models for every task. As long as frontier model advancement continues to advance inference costs (which seems to be the case as parameter sizes grow at the largest scales), there will be incentive to carefully decide which tasks can be done by which models. At some point, the irrational exuberance around AI investment and spending will give way to rationality – but to distort the Keynes saying, it can remain irrational much longer than we can bear the cost…(More)”.

Book by Alessandro Crimi: “This book explores the synergy between artificial intelligence and medtech, offering a roadmap toward achieving the United Nations’ Sustainable Development Goals. These technologies are part of a wave of innovation transforming how we learn, work, communicate, and live, from self-driving cars to AI-designed drugs and quantum computing. Yet most advances remain concentrated in high-income countries, leaving many low-income nations struggling to keep up.

I examine the social, political, and economic factors limiting development, highlight successful strategies, and explore AI and biotechnologies. Through case studies, the book shows how these tools can improve healthcare access, promote sustainable agriculture, and tackle other global challenges while considering ethical implications. By focusing on solutions accessible to low-income countries, it offers insights for innovators in middle- and high-income nations launching ventures with limited resources.

Future trends rely on interdisciplinary approaches integrating economy, sociology, and technology, recognizing that climate change, new health challenges, demographic shifts, and technologies are reshaping societies worldwide. Developing and advanced economies are deeply interconnected, making this broader perspective essential…(More)”.

Article by Denice Ross & Chris Dick: “Federal data benefit American lives and livelihoods in ways most people never see, touching every corner of our lives. This includes a farmer pricing a crop, a county planning a hospital, a business siting a warehouse — all of these decisions use federal data. Other data save patients money by identifying generic drugs that can replace more expensive brand names, help airplanes avoid deadly bird strikes, and warn consumers about recalls of dangerous products. 

Because these data are mostly invisible, their disappearance is invisible too — that is, until we need the data and they aren’t there anymore.

As federal data policy nerds, the question we get asked all the time is How much data has the current administration terminated?

The answer is that it depends on what we count as “data” AND what counts as a termination. That’s not a dodge – working through those two critical nuances is the substance of this piece.

The answer also depends on what the information will be used for. Ours is a data policy question, so we looked for structured, numerical datasets that have been terminated – meaning there will be no collections of those data in the future. Defining terminations that way lets us ask how agencies consulted with the public about a dataset’s value before ending it, and what its loss means for the federal government’s ability to serve the American people.

The purpose of the Federal Data Terminations Tracker is to be the most policy-relevant, verified accounting of federal data terminations available.

To create this Tracker, the dataindex.us team identified dozens of federal datasets and hundreds of data elements that have been terminated – significantly fewer than other reports, but more tailored to informing future data policies needed to run a modern society. These are data that have long underpinned policymaking, journalism, advocacy and research that improve American lives and livelihoods. These figures will change as terminations continue, collections are merged, and as court orders restore data. 

Want more details on why this question about federal data losses is so tricky? Read on. Want to see what data have been terminated? Visit the Federal Data Terminations Tracker at dataindex.us/terminations-tracker…(More)”.

Article by Rachel Santarsiero: “…The fossil fuel industry is trying to weaken methane reporting requirements while simultaneously fighting to preserve the related federal emissions database it relies on for credibility, according to U.S. Environmental Protection Agency (EPA) records recently released in response to a freedom of information request.

When the Trump administration announced plans last year to repeal the federal government’s greenhouse gas reporting system, rather than celebrate, many oil and gas companies publicly urged the EPA to preserve it. But for more than a year, behind closed doors, the fossil fuel industry has been pushing to weaken one of the reporting program’s most consequential elements.

Oil and gas companies fear this Biden-era revision to the methane reporting rule known as Subpart W, would force them to disclose much higher — and previously hidden — pollution levels. Those concerns, brewing for years, grew into a wave that ultimately would break onto the friendly shores of the Trump EPA in response to the agency’s expected overhaul of both the methane reporting rule and the Greenhouse Gas Reporting Program (GHGRP), the most comprehensive system for tracking the nation’s greenhouse gases, industry comments and public records show.

At a private trade group meeting in 2023, one energy analyst said the revised methane rule would be a “major PR headache” for oil and gas companies. “How do we even go talk to our investors and explain that this is what’s happening?” he said. “Things that historically potentially had gone unreported will now have to be reported.”

In 2025, an industry consultant predicted that some companies could see their reported methane emissions increase by four to ten times under the rule’s new methodology. As recently as this spring, another consultant speaking to a gas group conference warned that updated disclosure requirements would increase reported methane emissions by roughly 16 percent. Last month, major gas producer EQT cited this revised rule as one reason its reported methane emissions rose in 2025…(More)”.