Stefaan Verhulst

Article by Eric Boodman: “The bug looked like a new species. It had the classic mark of the dead-leaf mantises — a flattened shield, brown and crinkled to mimic forest-floor detritus — but the camouflage was almost too good, as if it were wearing an actual leaf. It was unlike any specimen Kris Anderson had ever seen.

Mr. Anderson has seen plenty of specimens. He has thousands pinned in drawers in his Las Vegas home, legs out, wings spread to reveal a stained-glass geometry of veins.

Every evening after his workday as a director of a counseling center, he spends two and a half hours poring over the 500 or 600 mantis pictures that enthusiasts have uploaded to the citizen science website iNaturalist, where anyone can submit photos of wildlife to help map biodiversity. To Mr. Anderson, most are junk — blurry shots of common species. But every so often there is something worth writing up: a mantis never before documented on Aruba, or a rare behavior, like three females seemingly eating each other.

For a few seconds, the mantis he saw on June 17 seemed like a taxonomic jackpot. Then he realized he’d been duped. This mantis was fake, a product of artificial intelligence…(More)”.

OECD Report: “Sharing data across organisations and jurisdictions generates economic and social value, increasingly so as progress in artificial intelligence depends on access to high-quality, domain-specific data. Data spaces promise trusted multi-party data sharing under federated governance, yet, despite significant public investment, few have reached operational and financial viability. Drawing on the analysis of more than 200 initiatives, including case studies of successes and failures, and an OECD Expert Workshop, this report examines where data spaces provide value that alternative data-sharing mechanisms cannot. In so doing, it illustrates how data spaces best suit settings with three or more parties, limited trust in any single intermediary, collective value creation and ongoing governance needs. Governance design, interoperability and trust-enhancing mechanisms underpin this value, but many face a bootstrapping problem, which eight policy considerations help address. The report supports OECD work on data free flow with trust (DFFT) and the OECD Recommendation on Enhancing Access to and Sharing of Data…(More)”.

Paper by Anil Arora: “National Statistical Offices (NSOs) were established to serve essential state functions—taxation, mobilization, and administrative governance—but evolved into professional institutions that safeguard the public interest through impartial statistics. Today, despite sustained modernization, many NSOs face declining survey participation, fiscal pressure, political encroachment, and mounting competition from private-sector and AI-mediated data actors. This paper is intended to stimulate discussion within the international statistical community and those dependent on high quality data, information and insights to make decisions. It does not seek to exhaustively catalogue the many national statistical organizations, national statistical institutes (NSIs), and professional associations engaged in innovation and reform to increase trust, relevance and impact. Rather, it highlights illustrative examples of innovation, leadership, and risk to underscore a central proposition: modernization of methods and technologies is necessary but not sufficient. Sustained trust requires visible independence, ethical leadership, proactive engagement, and institutional courage. Drawing on international initiatives led by the United Nations Statistics Division, OECD, Eurostat, the International Association for Official Statistics (IAOS), the International Statistical Institute (ISI), and the American Statistical Association (ASA), among others, as well as modernization leadership within several advanced statistical systems, the paper proposes a comprehensive reform agenda for democratic resilience, notably in an emerging AI-mediated information ecosystem…(More)”.

Essay by Preston Lee: “My generation has never experienced politics untouched by algorithms. Every election I have been old enough to follow has unfolded, at least in part, on social media. From Obama’s historic victory in 2008 to Trump’s unconventional win for a second term in 2024, these campaigns showed how political campaigns compete not only for votes but also for attention, likes, and shares. 

Very few people today question whether artificial intelligence will affect elections. The debate has shifted from “if” to “how.” The more urgent question is how artificial intelligence will shape voters’ political habits, particularly through the spread of disingenuous disinformation and the rise of political apathy. If AI erodes public trust in the democratic system and convinces citizens that their participation no longer matters, it threatens election outcomes and the legitimacy of our democracy as a whole.

The root of a healthy democracy is shared ideals that allow disagreement to occur without undermining the system itself. Melissa De Witte, Deputy Director of Social Science Communications at Stanford, gathers perspectives from experts and conveys in her article how healthy democracies need discourse and disagreement. She speaks with Debra Satz, Dean of the Stanford University School of Humanities and Sciences, and Paul Brest, Director of the Law & Policy Lab at Stanford Law School. Satz shares, “Disagreement can be a space that allows sharply different voices to be heard, but only when there are established processes and procedures in place to handle conflict when it arises and norms of openness, humility, and mutual respect.” Mutual trust bridges access and participation, and it is fundamental to democracy because, as defined by the Youth Democracy Cohort, our “democracy depends on participation,” and if trust deteriorates, cynicism increases while promoting political apathy. When people no longer trust or support these systems, where do they turn? 

The most dangerous consequence of the rise of AI is that it may convince people that there is no point in making their own decisions in the first place. Artificial intelligence has created a world where information can be broadly distributed, but it can also be manipulated for political persuasion. Now, speeches can be written in seconds, and political conversations can be generated to mimic the voices of political figures, without direct human participation. Its rapid advancements have created an environment in which citizens question the authenticity of information, leading to disengagement across the nation. Yet, when 81% of Americans feel that crucial election content will be rife with misinformation because of artificial intelligence, it becomes increasingly hard to trust the integrity of democracy…(More)”.

Article by Paula Oliver Llorente: “Since the appointment of the European Commission’s first Executive Vice-President for Tech Sovereignty, Security and Democracy on 1 December 2024, technological sovereignty has become a fashionable term in the corridors of Brussels, Berlin, Paris and every other European capital, including Madrid. Yet this has happened without a clear definition of the concept.

Despite this shortcoming, the European Commission has sought to give the concept substance and meaning through accomplished facts. Just a few weeks before the summer, it unveiled its long-awaited technological sovereignty package, which, in simplified terms, understands technological sovereignty as possessing the digital capabilities needed for our society to function while minimising strategic dependencies and the risks of external interference. The package was necessary and includes highly relevant measures for the Union’s technological sovereignty, but it also clearly reflects many of the EU’s unresolved dilemmas. However, if one critical flaw had to be singled out, it would be that it neither rests on nor addresses one of the essential elements of technological sovereignty: agency, that is, the capacity to decide what the digital future into which technology will be integrated should look like…(More)”.

Expert consensus report by Panayiota Kendeou, Jeffrey A. Greene, and Nia Nixon: “This report documents the findings and methodology of the consensus research project on Generative AI’s impact on learning in K-20 U.S. education. It is intended for researchers, educators, policymakers, and other stakeholders seeking evidence-based guidance.

Generative AI (GenAI) has been described as both the solution to humanity’s problems and also its greatest threat. In the context of education, those who look to research find mostly low-quality studies. Recent expert reports describe the risks of GenAI use but don’t tie them to specific learning processes. Experts in learning and education examined where GenAI helps learning, where it harms it, and what to do about it. They identified 65 learning processes GenAI can affect and rated how much GenAI could disrupt or enhance each one. Consensus was reached on 42 of the 65 processes…(More)”.

Essay by Niall Ferguson, Lisa Klaassen, and Eric Schmidt: “In the span of a few years, China and the United States have each assembled something future historians might recognize as a new kind of empire. Their dominions extend not merely over territory, trade routes, and airspace but over knowledge itself. By controlling the large language models, chips, physical infrastructure, and capital that provide the foundations for advanced machine cognition, Beijing and Washington are not only attaining new heights of economic performance and power projection; they are also setting the terms of all future scientific and intellectual progress. The term “AI superpowers” fails to capture the influence and reach that these rivals now wield. Even without active alliance building or coercion, they can make all other countries that seek access to advanced AI beholden to them.

Imperial history carries a warning. In the absence of other forces, empires seldom succumb to a challenger or to barbarians at the gates. More often, they fail because defending their vast dominions drains them dry, whether of material resources, the consent of the governed, or the loyalty of elites. The imperial center finds it can no longer maintain control over the periphery—the regions that once submitted to it. Failure can be financial, as it was for the British Empire after 1945, when the debts of two world wars forced London to liquidate its imperial assets to pay the price of victory: a welfare state for the citizens of the imperial metropole. It can be institutional, as it was for the Qing empire in the late nineteenth century, when it failed to industrialize, reform its bureaucracy, and renew the legitimacy of the Manchu dynasty.

Or it can be technological. The Royal Navy’s Dreadnought of 1906 gave the United Kingdom the first “all big gun” battleship and, with the same stroke, rendered its existing battle fleet obsolete. Overnight, this breakthrough opened the way for rivals such as Germany, erasing a century of accumulated British advantage. The resulting arms race forced the United Kingdom to nearly double naval spending between 1900 and 1914 and clashed with the Liberal government’s new social spending commitments, such as the Old Age Pension Act of 1908. The decisive question in the contest between Beijing and Washington may therefore not be who achieves artificial general intelligence first, but who can bankroll the race.

AI is imperial in two ways. On the one hand, China and the United States had empire-like qualities before the AI race began: Washington, the incumbent, pairs global military primacy with an alliance system no rival can match. It also supplies the currency for the lion’s share of international trade and transactions. Beijing, often described as the challenger, combines industrial and supply chain dominance with the world’s largest navy and a tightening grip on the physical and digital infrastructure of countries across Africa, Asia, and Latin America. For both, AI is a way to extend the power they already wield. But AI is also by its nature an imperial substance. Whoever produces and controls frontier models acquires global leverage over innovation, production, science, culture, and, ultimately, advanced thought. Much as silver underwrote Habsburg Spain and oil defined the “American century,” this new commodity will not merely enrich the powers that dominate it but also provide the basic operating system on which everyone else must depend…(More)”.

About: “V-Dem provides a multidimensional and disaggregated dataset that reflects the complexity of the concept of democracy as a system of rule that goes beyond the simple presence of elections. We distinguish between five high-level principles of democracy: electoral, liberal, participatory, deliberative, and egalitarian, and collect data to measure these principles.

The V-Dem Dataset – Includes the world’s most comprehensive and detailed democracy ratings. The latest version of the dataset and associated reference documents can be downloaded free of charge. Download the data – Each ZIP includes: Dataset, Codebook, What’s New, Cautionary Notes, and Suggested Citations. Download our datasets and reference materials…(More)”.

Article by Erik Brynjolfsson, Anton Korinek, and Ajay Agrawal: “AI has improved radically in recent years and is poised to become more powerful. Our institutions, organizations, skills, and economic models are struggling to keep pace. In this growing gap lie the greatest risks and challenges of the coming decade, as well as the greatest opportunities. We urgently need to improve our understanding of AI’s economic implications.

Key Insights

• Transformative AI (TAI) is advancing faster than our economic institutions can adapt, creating both unprecedented opportunities and significant risks that demand immediate attention.
• The implications of TAI are affecting almost all areas of economics and are posing fundamental questions about economic growth, science and innovation, income distribution, decision-making power, geoeconomics, information and knowledge, safety risks, and even our sense of meaning and worth.
• In this rapid transitional period, interdisciplinary collaboration between economists, computer scientists, and policymakers is crucial for developing the tools needed to align TAI with human well-being and prevent exacerbation of existing inequalities.
• Traditional productivity measures and GDP miss AI’s full economic impact. New economic measures must be developed to understand and prepare for a rapidly changing and fundamentally different economy.

Before we can formulate effective solutions, we must define the fundamental questions, laying the groundwork for a productive research agenda. By doing so, we seek to catalyze the insights and analytical tools necessary to navigate AI’s evolving landscape while fostering shared prosperity. A question well-posed is half answered…(More)”.

Article by Danai Nesta Kupemba: “Big Tech’s newest personal agents — Meta’s Muse and OpenAI’s Dots — promise to book tables at restaurants, buy groceries, and carry out other types of digital grunt work for users.

Consumers are opting in. Muse has been downloaded 9 million times in the U.S. and Canada since its launch on September 8, market intelligence company AppMagic, told Rest of World. As many as 1.8 million people use the app daily. 

Meta aspires to a global launch. “[We] are quite excited to bring personal agents to billions of people around the world,” Alexandr Wang, Meta’s chief AI officer, told the Financial Times.

Some startups are already building agents in markets that Big Tech hopes to reach. For instance, South African startup ManyHands offers Manny, a personal assistant designed to order groceries, book appointments and make payments through WhatsApp. But adapting agents to local conditions has its challenges. Anton Moulder, ManyHands’ founder, told Rest of World that getting the AI assistant to converse in all of South Africa’s 11 official spoken languages has been difficult.

Rest of World spoke to AI researchers and other experts to understand how these personal agents would perform outside Western nations.

Would AI agents understand the local language and culture?

English speakers could benefit more from AI agents, which run on top of large language models that underperform in many non-English languages. 

“Linguistic inclusion is ultimately about power,” Payal Arora, professor of inclusive AI cultures at Utrecht University and the founder of Inclusive AI Lab, told Rest of World.

Meta has said that expanding Muse internationally would require support for local languages and integration with local services.

AI agents would also need to understand the local culture, including the unique ways people communicate and bargain. “In many parts of the Global South, meaning is deeply contextual, shaped by family, community, religion, local institutions, and informal social networks,” Arora said.

Without that understanding, agents might impose Western assumptions on their users, Usha Raman, an independent researcher on AI policy and digital culture, told Rest of World. “The danger then is that existing forms of cultural and economic hegemony continue and the rest of the world is forced to adapt,” she said…(More)”.