Stefaan Verhulst

(Open access) book edited by Riku Neuvonen and Jukka Viljanen: “We live in a world where many things happen digitally. Ones and zeros determine everyday actions and the course of life. The current digital world is two-dimensional. We see, hear and influence it using tools that are clearly part of the physical world. In the current digital world, we see things on screens. During this millennium, screens have become smaller, and in addition to computer monitors, virtual worlds can now be viewed on the screens of tablets and smartphones. Hardware and devices delivering augmented reality experiences are already on the market, as are virtual reality glasses. Headsets of different types are already commonplace.

A real virtual world is one that we experience like the physical world. In this book, we imagine the potential of such worlds and we examine these from the perspective of law and related disciplines. The current digital world has led us to questions about the applicability of rights. In theory, the regulation of the physical world also applies to the digital world. In practice, this is not always the case, as problems associated with the protection of privacy but also disinformation, online hate and others have emerged. In this context, we can talk about digital human rights, digital rights and digital constitutionalism.

The problems we face in the digital world are related to access, access to information, and privacy. The virtual world has the same problems, but virtuality as an all-encompassing experience also creates new challenges. In the age of platforms, algorithms have played a key role in displaying content and users are now shown what they or advertisers think they want to see. Various forms of artificial intelligence are also involved. These opportunities and problems of the platform era will also transfer to the virtual worlds of the future. This is especially the case when the advocates of virtual worlds, such as the metaverse, want them to be worlds where users spend a significant portion ofp. 2their time. These questions will be approached and explored from several different starting points.

This book is about considering, imagining and reflecting on virtual worlds. In this introduction, we will establish a picture of what virtual worlds can be, largely based on entertainment, films, books, television series, and games. This basis is logical since these forms of entertainment have been creating and experimenting with virtual worlds for decades. After establishing this picture, we will provide an overview of the research that deals with rights in virtual worlds before introducing the authors of the chapters and how each will approach this challenging theme from different perspectives…(More)”.

Report by Pew Research: “Americans have become increasingly worried about artificial intelligence over the years, and young adults’ concern has continued to climb. Worry over job loss, too, is on the rise…

Americans have grown more concerned about AI over time

% of U.S. adults who say the increased use of artificial intelligence (AI) in daily life makes them feel …

Source: Survey of U.S. adults conducted June 22-28, 2026.

Today, 52% of Americans say they are more concerned than excited about the increased use of AI in daily life – up from 37% in 2021. Another 9% are more excited than concerned and 37% say they’re equally excited and concerned, according to a Pew Research Center survey conducted June 22-28, 2026.

Concern about AI is up among younger and older Americans alike since we first asked this question in 2021. But while the rise was mainly in the first two years for Americans ages 30 and older, concern continues to climb for adults under 30 – whose skepticism of AI has made headlines in recent months…(More)”.

Article by Urs Gasser, Viktor Mayer-Schönberger, and Fabienne Marco: “The current approach to artificial intelligence oversight is largely built on measurement. Benchmarks assess capability, red teams probe for failure modes, and evaluation frameworks certify safety and legal compliance before deployment. These instruments can be valuable, but they also share a critical structural vulnerability that AI governance has not yet adequately absorbed: The measurements used to verify AI models are not external to the objects being measured. Rather, this program of measurement operates within the same ecosystem—and is shaped by the same competitive pressures and institutional incentives—that produces the AI technologies it is meant to assess. A result is that the measured behavior of a model may diverge significantly from its behavior when deployed.

Put simply, as AI systems and the organizations building them learn what evaluators look for, the AI model performs for the test, figuring out how to excel in benchmarks without necessarily becoming safer, more useful, or more reliable in real-life scenarios. As evaluation increasingly assesses only the model’s capacity to ace that same evaluation, the boundary between system and oversight grows porous.

This kind of situation is known as a measurement trap: When a measure becomes a target, it ceases to be a good measure. Measurement traps are not unique to AI. Standardized testing in education leads schools to “teach to the test” rather than help students develop critical thinking skills. In software development, when productivity is tied to the number of lines of code written, programmers write long, repetitive code, which may or may not be good software. And in business, when bonuses are tied to revenue measures, managers prioritize short-term sales over long-term profitability…(More)”.

Article by Mehr Demokratie: “In autumn 2026, a randomly selected Citizens’ Panel will discuss measures to strengthen democracy in the European Union (EU). Until the end of the year, all EU residents can submit their questions and ideas on the subject online and at events organised on the topic… Perspectives on key aspects of democratic life, such as access to reliable information, media literacy, awareness of citizens’ rights, participation in decision-making, and the skills needed to engage in democracy in a digital world can be contributed. 

This debate follows up on the European Democracy Shield. It will feed into ongoing and future EU initiatives aimed at strengthening democracy and will inform future participatory processes, including a European Citizens’ Panel on Democratic Resilience. 

The Citizens’ Forum will meet from September to November 2026. Contributions from the online debate and events will feed into the Forum. The mini-public will discuss the contributions from the online debate and the in-person events and make recommendations to the European Commission….

From 22 May to 7 June 2026, the Sortition Foundation was on the road to recruit participants for the Citizens’ Forum. In collaboration with local partners across the EU, they knocked on doors and approached citizens from 150 randomly selected locations in all EU countries.

From the original pool of interested individuals, the final group of Citizens’ Assembly members will be selected at random to reflect the diversity of the EU population, taking into account gender, education, occupation and the spectrum between rural and urban areas…(More)”.

OECD Dashboard: “Rising uncertainties and geo-political tensions, together with more complex trade relations, have increased the demand for timely data and tools to monitor global trade. At the same time, advances in Big Data analytics and access to large quantities of alternative data – outside the realm of official statistics – have opened new avenues to track trade developments. These data can help identify bottlenecks, disruptions and emerging trends in near real time, but need to be carefully cleaned, validated and interpreted.

One such alternative data source is the Automatic Identification System (AIS), developed by the International Maritime Organisation on to facilitate the tracking of vessels across the globe. The system includes messages transmitted by ships to land or satellite receivers and is available in quasi real time. While AIS was primarily designed to ensure vessel safety, it is particularly well suited for providing insights on trade developments, as over 80% in volume of international merchandise trade is carried by sea. AIS data also provides granular vessel information and detailed location data which, when combined with other data sources, can support analysis at the country, port and berth levels, by vessel type, product group and trade flow.

New work from the OECD Statistics and Data Directorate builds on previous OECD research using AIS data. It refines the identification of ports by exploiting information at the berth level and combines AIS vessel movements with satellite imagery and a rule-based approach to map maritime activity to 23 commodity groups worldwide. This makes it possible to derive timely, experimental estimates of trade by product, with broad global coverage across ports and berths.

The updated OECD AIS Tracking Dashboard visualises key indicators on maritime activity, ports and trade flows. It retains the country-level indicators on vessel activity, capacity, trade estimates and efficiency measures, while adding new product-level breakdowns that allow users to compare developments across selected commodity groups and identify the main ports for imports and exports. The dashboard also includes a new chokepoints section, currently covering Suez Canal and the Strait of Hormuz, where users can monitor vessel composition and product-level flows through selected strategic maritime passages…(More)”.

Paper by Suoyi Tan et al: “Poverty mapping is increasingly important for monitoring Sustainable Development Goal 1 (SDG 1) of the United Nations 2030 Agenda, which aims to end poverty in all its forms everywhere. Yet timely and fine-resolution poverty estimation remains difficult because conventional census- and survey-based approaches are costly, infrequent, and often sparse precisely where deprivation is most severe. As poverty emerges from complex socioeconomic systems shaped by human mobility, social interactions, infrastructure, and economic activities, emerging computational methods and nontraditional data sources have created new opportunities for poverty estimation and mapping. At the intersection of statistical physics, complex systems science, and data science, these approaches enable poverty estimation at finer spatial and temporal resolutions. This review summarizes the main concepts of poverty and the principal frameworks used to measure it, and examines recent advances on poverty estimation and mapping using satellite imagery, mobile phone data, social media data, and multisource data fusion. The review also discusses persistent challenges related to representativeness, transferability across regions, interpretability, and uncertainty quantification. Finally, the review clarifies both the analytical promise and the practical limits of contemporary poverty mapping…(More)”.

Paper by Levin Brinkmann et al: “Intelligent machines have the potential to uncover problem-solving strategies beyond human discovery. Emerging evidence from competitive gameplay, such as Go and chess, demonstrates that AI systems are evolving from mere tools to sources of cultural innovation adopted by humans. However, the conditions under which intelligent machines transition from tools to drivers of persistent cultural change remain unclear. We identify three key dimensions that modulate machine influence on human problem-solving: the discovered strategies must be non-trivial, learnable, and offer a clear advantage. Using a cultural transmission experiment, we demonstrate that when these conditions are met, machine-discovered strategies can be transmitted, understood, and preserved by human populations, leading to enduring cultural shifts. Conversely, using agent-based simulations, we show how machine influence is constrained in the absence of these conditions. These findings provide a framework for understanding how machines can persistently expand human cognitive skills and underscore the need to consider their broader implications for human cognition and cultural evolution…(More)”.

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)”.