Stefaan Verhulst

Article by Chana R. Schoenberger: “Seeing an AI disclosure on a social post leads users to disengage, because they feel the creator spent less effort on making it….No one thinks that content creators posting on social media are speaking directly to them. But knowing that the creator used generative AI to make their video or image, rather than pouring their own effort into it, drives users to feel less of a connection with the creator, and to engage less with the post.

A new study finds that seeing an AI disclosure on a social post leads users to feel the creator spent less effort on making it, causing it to lose authenticity. The authors from the University of Southern California’s Marshall School of Business—Stephan Carney and Ignacio Riveros, both doctoral students in marketing, and Stephanie M. Tully, associate professor of business administration—see implications for how influencers, brands, and social media platforms deal with the widespread and growing use of AI for post creation.

Social media creates parasocial connections, “one-sided emotional bonds between consumers and creators,” in which users feel a kind of attachment to the person creating the posts, the authors write. When users see an AI disclosure on a post, they take that to mean the creator didn’t work as hard on it, and they lose their appetite to engage with the video. But if they see an AI disclosure worded to indicate that the creator did put work into the post, the effect isn’t as strong, the researchers found.

The study evolved out of a broader question about how AI is changing consumer decision-making, Carney says. At first, the researchers started looking at whether people would feel more comfortable with a social media post knowing that AI had been used to make it.

They theorized that a more transparent approach would help, but instead they discovered that knowing that AI was involved in a post’s creation made users uninterested in it. “They’re not likely to engage with it or think that it’s good,” he says. This finding held regardless of the user’s age, so it wasn’t related to generational familiarity with the technology.

The question became: What causes this effect? It wasn’t because the quality of the content was lower when AI was used; in fact, both types of content had similar quality levels. It also wasn’t general distrust of AI or anger at being deceived by AI. Instead, the researchers found that AI use disrupted the attachment that users had to the creator of the content…(More)”.

Article by Bloomberg Cities: “Local governments have never been more focused on delivering at the pace and with the precision that people need. Yet their efforts are often slowed down by “legacy tech”—a creaky permitting tool or aging data catalog, for example—that is considered too critical, too complex, or too expensive to upgrade. 

Now artificial intelligence is helping bridge the gap, as local leaders are beginning to tap the technology not only to power new products and services, but also to map their internal technology and data architecture. And while this work might sound like a strictly back-office exercise, the implications are much broader. That’s because this kind of AI adoption can provide cities with insights that once took significant time and, in some cases, outside expertise to produce, giving them more options for how to modernize systems and improve services.

“In every city, there’s at least one resident-facing service hanging on a 20-year-old piece of technology,” explains Carrie Bishop, who oversees data programs for the Government Innovation team at Bloomberg Philanthropies. “Now, local leaders can use AI to understand that technology, improve or replace it, and, in doing so, unlock change in peoples’ lives.”

While this work is still in its early stages, efforts by teams in Austin, Texas, and Belo Horizonte, Brazil, already are surfacing insights cities everywhere can put into action…(More)”.

Article by Hélène Landemore and Audrey Tang: “Those building the most powerful AI systems in the world are increasingly telling us that the race to build them may be moving too fast. But there is a problem: They cannot simply agree to slow down.

On Sept. 12, Anthropic CEO Dario Amodei called for an easing of the pace at which AI models improve and committed his company to giving independent, third-party evaluators ongoing, employee-like access to its systems. Amodei further gestured toward “democratic coordination” on shared safety limits and “global coordination” on the international stage. Within hours, OpenAI CEO Sam Altman agreed that “we need to pace the frontier” and made the same evaluator pledge. Elon Musk’s response was three words: “Dario is right.”

This apparent convergence among the leaders of the frontier AI labs is striking. It comes after months of mounting concern inside and outside the industry: AI models have begun to find ways around safeguards and test environments; researchers have left frontier labs amid disagreements over catastrophic and extinction-level risks; and more than 1,300 employees at leading AI companies have called on Washington to back an international slowdown.

But even if the CEOs agree, the race does not stop.

The reason is structural. The two governments with the greatest capacity to shape the frontier AI race — in Washington and Beijing — have powerful incentives not to be the first to slow down. Each has reason to fear that restraint on their part will simply hand a strategic advantage to the other.

What we are facing, in other words, is a double collective-action problem, one nested under the other. The two are linked: “If we slow down, China wins” is the main argument American labs and government officials use against binding regulation. The outer race is the inner one’s best excuse; end the global race, and the domestic one loses cover. The good news is that, contrary to frequent depictions, neither is a simple prisoner’s dilemma, in which each player’s best strategy is to defect no matter what others do, and also that the only solution is necessarily some sort of global regulatory leviathan.

Instead, both collective-action problems look more like what game theorists call a “stag hunt,” after a parable of Jean-Jacques Rousseau’s. A party of hunters sets out after a stag. It will take all of them to bring it down, and it will feed them all for days. Along the way, each hunter sees a hare and considers catching it. The hare is a thin meal but a sure one, though if even a single hunter breaks off to chase one, the stag escapes and the rest go home hungry. Nobody in the party prefers the hare. A hunter settles for it only after losing faith that the others will hold the line in the quest for the stag…(More)”.

Report by David Caswell and Shuwei Fang: “AI will likely grow the marketplace for information more than it has grown in 500 years, and could radically expand the role of information in human life. This opens the possibility of a society in which many more people can make sense of more situations, navigate change, and act with greater confidence and understanding.

Realizing that potential requires an information ecosystem that produces reliable knowledge and connects it with the circumstances where it is useful. This report examines what that would involve: how AI-mediated information might be gathered, verified, combined with context, and turned into relevant experiences, better decisions and practical action.

It draws on four Signals at Scale summits, held between December 2025 and May 2026, involving approximately 140 participants from technology, investment, governance, academia and media. Discussions began with the needs of people and societies, then worked toward the capabilities and investment required to meet them.

The resulting analysis identifies 14 areas of value creation and 70 potential investment categories. These span original information gathering, persistent knowledge, verification and integrity, personal and community context, decision support, and the infrastructure connecting them. Together, they describe opportunities for using AI to make useful knowledge radically more accessible, responsive and relevant—and to support forms of individual and collective understanding that are difficult to achieve today.

This is an exploratory map, with provisional categories and many questions that remain unresolved. Its purpose is to give builders and funders a considered starting point for practical work: identifying, developing and testing the systems through which AI-mediated information could bring substantial and lasting benefits to all of us…(More)”.

Report by International IDEA: “The findings of the 2026 Global State of Democracy (GSoD) report reflect what the Institute for Economics and Peace has called a “Great Fragmentation” (IEP 2026: 2) of historical alliances and partnerships, compounding a long-standing climate of radical uncertainty (Casas-Zamora 2024). As historical political alliances are questioned, new blocs emerge, and safety and security wane around the world, democracies are struggling to lead the way ahead in a decisive and authoritative manner.

Notably, one of the clearest trends in the Global State of Democracy Indices dataset is stagnation: in 81 percent of cases in which shifts at the factor¹ level were possible, there was no change in countries’ performance. Since current levels of performance are largely middling, with most countries continuing to be clustered in the mid- and low-performance ranges across all categories, this stagnation is concerning. The relative dominance of mid-range performance is particularly worrying because it suggests that many countries remain in politically fragile “in-between” conditions. As discussed in Part 2, such contexts, especially those at the lower end of the mid-range band, are associated with heightened risks of instability and conflict (Gleditsch et al. 2009).

When change in the quality of democracy did occur, it was more often negative than positive. In 2025, 98 countries—representing 57 percent of all countries assessed—suffered a decline in at least one factor of democratic performance compared with their own performance five years earlier. In contrast, only 55 countries (32 percent) advanced in at least one factor over that period.² This represents a substantial shift compared with a decade earlier: in 2015, 31 percent of all countries experienced at least one decline, and 27 percent saw at least one improvement. In 2025, deterioration was concentrated across many of the fundamental building blocks of democratic systems—credible elections, effective legislatures, freedom of expression, freedom of the press, and access to a fair legal system in the pursuit of justice.

The most extensive global decline occurred in Freedom of Expression, which impacted 42 countries (24 percent of all countries in the dataset). This was followed closely by Freedom of the Press, whose scores fell in 23 percent of countries, and Access to Justice, which declined in 18 percent of countries. Much of the deterioration is rooted in coups, conflict, and the centralization of power, and the ongoing challenges affecting some of the most fundamental aspects of democratic governance raise questions about the extent to which the mechanisms relied upon to channel public priorities into policy remain fit for purpose…(More)”.

Article by Juliane Schmeling: “This article focuses on government collaboration in sovereign data spaces. As data sovereignty becomes increasingly crucial in a data-driven era, the need for controlled data-sharing environments, exemplified by initiatives such as Gaia-X, gains prominence. The article explores research-practice gaps, drawing on a focus group with key pioneers in the development of public-sector data spaces in Germany, who assessed the core focus areas of digital government research in this field. Findings highlight the roles of organisational capabilities, innovation, and participation, underpinned by trust and transparency, as well as the importance of standardisation and governance in fostering sovereign data spaces. The article concludes by emphasising actionable recommendations for enhancing data collaboration while addressing legal and institutional barriers. In doing so, the article contributes to the understanding of data sovereignty and its implications for public bureaucracies, ultimately aiming to reflect the digital government literature from a practical implementation perspective…(More)”.

Paper by Lorenzo Manuali: “An increasingly large number of projects seek to use Large Language Models (LLMs) to enhance or support democracy. I argue that many of the ways in which computer scientists, deliberative democratic practitioners, and others are using LLMs to do this threatens the non-instrumental value of a collective’s ability to determine its own future. In particular, I present a novel worry that projects aimed at algorithmically facilitating deliberation and representing people’s interests in political processes threaten what has been called democratic autonomy. I begin with some conceptual groundwork concerning collective self-determination (and specifically democratic autonomy) to motivate its non-instrumental value. Next, I offer a few necessary conditions for democratic autonomy from the literature, such as the possession of a joint intention and said joint intention being realized in policy at least some significant portion of the time. I then show when two kinds of projects that use LLMs to enhance democracy – facilitative and representative LLMs – threaten these necessary conditions and thus democratic autonomy. I conclude by outlining some practical upshots and recommendations for projects that aim to use LLMs to enhance or support democracy…(More)”.

Paper by Fergus Green and Michele Zadra: “Meeting the goals of the Paris Agreement on climate change demands rapid and systemic economic transformation. Yet, contemporary democracies seem tragically ill-suited to this task. Proposals to insulate climate policy making from democratic control—for instance, through delegation to expert bodies—seem likely to fuel the rise of authoritarian populists, which is already eroding democratic norms and institutions. Deepening democratic engagement with the process of decarbonization may provide an escape route from this apparent “democracy–decarbonization dilemma.” In this vein, there has recently been much enthusiasm for climate assemblies. Such processes can have profound effects on their participants. But can they influence attitudes to democracy and support for ambitious climate policy among the wider public? We conduct a narrative review of the theoretical and empirical literature on democratic mini-publics (DMPs) to answer this question and critically reflect on our findings. We find that DMPs can influence the wider public’s attitudes toward democratic institutions and public policy to some extent. Yet, DMPs alone—even if well publicized—seem unlikely to be sufficient to stimulate the widespread shifts in knowledge, capacities, attitudes and behavior necessary to escape the democracy–decarbonization dilemma. We propose expanding the use, institutionalization, and orientation of DMPs—so that they become a widely recognized and routinized feature of deliberative systems, interacting with both governments and the wider citizenry—while advancing complementary democratic reform initiatives…(More)”.

Report by the Tony Blair Institute: “Strong political leadership has always played a decisive role in determining which states succeed and which do not. This is especially true during times of disruption. Decisions made during these periods are often urgent and based on limited information, and their outcomes can have long-lasting effects on a country’s future trajectory.

Today, every leader is facing this scenario. Artificial intelligence will reshape every aspect of our world, from how wars are fought and labour markets operate to how science and research are conducted. Capabilities at the frontier are advancing almost weekly. Leaders are therefore confronting a technology whose consequences will extend across almost every area of government.

Yet while AI’s potential is widely understood, how it will evolve is not. Leaders know they need to act, but what to do and how much to do are often less clear. They must decide how much energy and compute to make available, how to prepare their economies and workforces, how to secure increasingly digital states, and how far to intervene in the development and deployment of the technology. In the West, they must make these choices amid a growing “techlash” against AI – but while doing less might help politically in the short term, it may carry significant long-term impacts on a country’s stability and security.

Political leaders cannot navigate this alone and are highly dependent on the institutions that support them, both to provide the evidence and expertise needed to understand the issues and potential decisions, and to implement those decisions. Yet the challenges that leaders face today often require multiple institutions, departments or regulators to work together, drawing on different strands of evidence and operating under different regulations. This increases the complexity of coordination and delivery. Leaders’ ability to navigate the age of AI will therefore depend on both the quality of the choices they make and on the capacity of the institutions around them to inform and implement those choices.

Technology, and especially AI, will be key to transforming how a state’s institutions work and improving their capacity to deliver. AI can help by enabling government to synthesise information across institutional boundaries, retrieve and apply institutional knowledge, and sustain coordination around shared outcomes. But technology alone is not enough. A new operating model is also needed that can combine political decision-making and authority with institutional expertise and is supported by abundant machine intelligence. Done well, this would give leaders greater capacity to understand the choices before them – and their potential consequences – while giving institutions greater capacity to translate those decisions into action…(More)”.

Article by Zeynep Engin, Jon Crowcroft and Stefaan Verhulst: “Academic peer review is in crisis—it is a structural reality that every editor, reviewer, and conscientious author now navigates daily. The symptoms are familiar: reviewer fatigue, inconsistent decisions, declining response rates, and a growing sense that the machinery of scholarly quality assurance is straining under a load it was never designed to bear. For a journal like Data & Policy—operating at the intersection of multiple disciplinary traditions and sectorial experiences, serving a field still in the process of constituting itself—these pressures are not abstract. They shape every editorial decision we make…(More)”.