Stefaan Verhulst
Book edited by Mark Findlay, and Noha Lea Halim: “In this volume, young voices craft a fresh exploration of living and working in virtual worlds. Using Alice in Wonderland as a guiding metaphor, contributors address profound and urgent questions: Who am I online? Who decides what is possible in virtual worlds? How can these spaces be made safe, fair, and empowering?
Through specially commissioned short essays and reflections, emerging voices with extensive experience of digital environments evaluate key challenges of governance and personhood within the metaverse. Drawing on diverse disciplinary insights, chapters investigate how design choices subtly function as constraints and possibilities, how identities transform across platforms and how communities build their own norms. Ultimately, the book emphasises the importance of treating the metaverse as a lived social space and prioritising the voices of its first-hand navigators. It outlines strategies for designing policies that protect individuals while simultaneously promoting agency, imagination and inclusion within virtual worlds like the metaverse, both now and in the future.
Governing Virtual Wonderland is an essential read for scholars, students, and those interested in regulation and governance in the new digital age. It is also useful for those across the disciplines of technology and AI, law, political science, philosophy of data science and computer science…(More)”.
Map by Power for Democracies: “AI has been reshaping how information spreads, how governments operate, and how citizens participate. Deciding where to direct limited resources to protect democracy from these rapid developments has been far from straightforward.
While various research communities have been working in parallel, producing valuable but fragmented analyses, no single resource exists that brings these findings together into a coherent body of work….
Given how fast AI is developing, we began filling these gaps as a priority. Over six weeks, our team reviewed a wide body of existing literature and selected ten leading frameworks mapping AI threats.
The selected literature spans multiple levels of abstraction, disciplinary lenses, and democratic contexts – from the Carnegie Endowment for International Peace’s broad survey of AI and democracy to the Brennan Center for Justice’s focus on US electoral integrity, to the global perspective of the 2026 International AI Safety Report.
We extracted and standardised entries from these sources into a unified, publicly available database containing:
- 144 documented AI threats to democracy
- 103 pro-democracy strategies for addressing them
- 44 independent opportunities for strengthening democratic resilience
Each entry is mapped onto the aspects of democracy it affects, using an adapted International IDEA framework of democracy that clusters around: citizenship, law and rights; representative and accountable government; civil society and popular participation; and a category covering international dynamics…(More)”.
Initiative by Patrick Grady: “For seventy years, prominent researchers, executives, and public intellectuals have offered specific dates for the arrival of artificial general intelligence, superintelligence, the technological singularity, and the collapse of large parts of the labour market. This archive collects those forecasts: both those the calendar has already refuted, and those still open but on the record.
The purpose is neither ridicule nor vindication. Public forecasts shape research funding, policy, and public expectation, and have a non-negligible impact on individual and societal welfare. It’s important to view present predictions within this context…(More)”.
Paper by John A. List, Matthias Rodemeier, Sutanuka Roy & Gregory K. Sun: “Behavioral interventions have become central to modern public policy, but their empirical promise remains contested because estimated treatment effects often appear small. We argue that a policy response is economically meaningful only relative to the response generated by alternative policies. We assemble more than 1,200 estimates from over 600 studies comparing “nudges” and traditional price interventions in the markets for cigarettes, alcohol, influenza vaccination, electricity, and residential water. Translating nudge effects into equivalent price changes, we find that behavioral interventions often correspond to enormous fiscal interventions, from an 11% tax on electricity to a 100% subsidy on influenza vaccinations. Nudges are also more cost-effective than price instruments in all markets, but cost-effectiveness does not predict the welfare ranking of policies. Using a behavioral extension of the Marginal Value of Public Funds, we show that nudges have high welfare returns at the margin, while price instruments often generate larger total surplus at scale…(More)”.
Book by Evangelos Pournaras, Srijoni Majumdar, Carina Hausladen, Dirk Helbing: “Interfacing Artificial Intelligence (AI) with democracy is one of the most profound challenges of our times. On the one hand, AI comes with opportunities to overcome long-standing challenges in democracy, such as low participation in deliberative and voting processes with poor representation of people. On the other hand, new risks arise from AI algorithms that are privacy-intrusive, biased, manipulative, spread misinformation and influence election results. Moving beyond the over-simplistic question of whether AI is good or bad for democracy, the Handbook on Democracy in the Era of Artificial Intelligence asks instead: how to upgrade democracies and the principles they are built on, using AI? How to engage with AI and on what terms? Which new values and design principles are required to build democratic resilience? In 34 chapters by 59 authors across the world from different disciplines, we explore how AI can empower collective intelligence for democracy (Part 1) and what is the future of deliberative democracy using large language models and social media (Part 2). We also illustrate the role of AI for building resilient self-governance systems (Part 3) and the challenges of transforming democracy in the age of AI (Part 4). We conclude with broader perspectives (Part 5) that re-imagine the interplay of democracy and AI…(More)”.
Book review by Luciano Magaldi Sardella of “The Credibility Crisis in Science by Thomas Plümper and Eric Neumayer: “…The scholarly landscape into which this book enters has been shaped by growing recognition of publication bias – the documented phenomenon that studies reporting positive or statistically significant findings are substantially more likely to be published than those reporting null results or failures to replicate. Research by Franco, Malhotra, and Simonovits found that among social science experiments that were peer-reviewed and approved before data collection, 91 per cent of published studies reported positive results, while only 72 per cent of the total conducted experiments did so. This systematic skewing of the published record, combined with growing awareness of questionable research practices (QRPs), has catalysed calls for reform across multiple disciplines. Plümper and Neumayer’s work addresses this ferment by providing a comprehensive taxonomy of how empirical findings become corrupted.
The authors’ central conceptual innovation lies in their tripartite taxonomy of empirical manipulation. They distinguish carefully between three categories often conflated in public discourse. First, outright data fabrication – the wholesale invention of findings – exemplified by the case of Diederik Stapel, the Dutch social psychologist who, between 2000 and 2011, fabricated data for at least 55 publications in leading journals including Science. Second, data manipulation – the alteration of raw data to produce desired outcomes. Third, and most significantly for their argument, “tweaking” – the intentional selection of research designs, model specifications, and analytical protocols based on the results they yield, often without explicit disclosure of these choices…(More)”.
Paper by Protiva Adhikary, et al: “Data sharing is often viewed as essential for transboundary water cooperation, fostering trust and reducing uncertainty. Yet, evidence shows that data exchange alone does not ensure collaboration or resilience. This paper examines India–Bangladesh water relations since 1972, drawing on 28 expert interviews and documents. Despite sharing 54 rivers, cooperation remains narrow and reactive – limited to select rivers, monsoon periods, and raw data. Bangladesh’s calls for year-round sharing contrast with India’s caution, revealing power asymmetries. The study argues that data sharing is inherently political, and meaningful cooperation requires institutional commitment to equity, transparency, and climate resilience…(More)”.
Article by Carlo Cordasco: “…What none of these critics could have told you was that anaesthesia would make possible open-heart surgery, organ transplantation, neurosurgery, and the entire architecture of modern surgical specialisation. The benefit was not a more comfortable version of what surgeons had been doing. It was the appearance of a possibility space whose contents were inconceivable from inside the surgical practice of 1846.
Cases of this kind have a particular structure. An innovation arrives whose costs are perfectly legible, since they are visible against the baseline of the practice it is reorganising and can be measured with the practice’s existing instruments, while its most profound benefits depend on practices, institutions and concepts that do not yet exist at the moment of evaluation. Serious critics with domain expertise document the costs, often correctly, since costs of that kind are precisely what the evaluative vocabulary they have inherited is built to measure. There is no possible evaluation in the critical moment that could capture the benefits, because the vocabulary that would describe them is itself one of the things the innovation is going to bring into being.
Take dating apps. Eli Finkel and others have shown that app-mediated dating can produce choice overload, weaken commitment formation, and reduce the development of relationship skills that older modes of meeting cultivated more naturally. The empirical literature on loneliness, declining marriage rates among the young, and the collapse of casual in-person romantic initiation is now substantial. The critics are documenting real things, and their evidence is solid in just the way Harrison’s evidence on anaesthesia was solid.
What this evidence cannot capture is the set of effects that have emerged through the existence of the apps themselves and that nobody arguing about Tinder a decade ago could have specified. Public space itself has been quietly transformed, since the existence of an alternative channel for romantic initiation has weakened the normative status of an in-person approach to the point where women navigate streets, cafés and workplaces in ways that would have been unrecognisable in 1995. Some of what used to be ambient harassment in physical settings has been redistributed onto platforms where it is at least more controllable, since digital interactions can be filtered, blocked, reported and audited in ways that street and workplace harassment cannot…(More)”.
Report by Mitchell Baker: “..An asset is “open source” or not based on its license terms. An open source asset can have a wide variety of impacts: public benefit or private enrichment; sustainable or a “flash in the pan;” research achievements or products that people use, to name a few. An open source asset’s impact depends on other layers of activity. There is no canonical artifact that sets out these layers of activity; the framework below is distilled from three decades of practice.
The first layer is the license, which determines whether or not something is actually “open source.” The definition of “open source AI” is under discussion, so the License Terms section below recommends a way to move forward despite the ambiguity. A second layer is the technology itself – is there a clear enough vision of what a project aims to build and the success criteria? A third layer is the team, which includes the people who build the project, the reasons they choose to participate, and what they need to give their best. A fourth layer is the collaborative process by which that technology is developed – is it a process in which multiple stakeholders in the EU share a vision, a community sense of ownership and a commitment to the whole? A fifth layer is the finished product – can the research and development advancements be turned into products that are used regularly and productively? A sixth layer is the organization that holds responsibility for the technology and/or products.
Two things about frontier AI are unlike the open source software experience this paper draws on: the capital required to build the largest models, and the structural challenges of the underlying hardware, chips, and compute. How frontier-scale training is financed in a public-benefit model is a genuinely open question, and this paper does not resolve it. The focus of this paper is different: whatever methods and amounts of funding are allocated, the likelihood of bringing the open source AI projects to successful impact depends on the variables discussed here. This paper will limit itself to the objectives for open source software and AI…(More)”.
Introduction to Special Issue by Roberto Falanga, Inês Campos and Doris Fuchs: “Worldwide, the growth of citizen distrust towards political institutions presents unprecedented challenges to representative democracy (Butzlaff and Messinger-Zimmer ; Vasilopolou et al. ; Wood). In recent decades, increasing disenfranchisement with standard avenues of political participation has prompted decision-makers and civil society organisations to experiment with new democratic practices aimed at empowering citizens in democratic policy and decision-making (Fung and Wright). Guided by theories of participatory and deliberative democracy, different countries, regions, and cities across the globe have developed innovative approaches, offering multiple channels of direct and indirect dialogue with elected representatives. The production of knowledge around such practices in different political contexts has gained significant traction over the last few years, and the multiplication of research outputs has certainly helped advance our understanding of democratic innovations (DIs hereafter).
The concept of DIs seeks to capture and make sense of participatory and deliberative practices that have spread significantly in Europe and beyond. As Saward early put it: ‘The phrase ‘democratic innovation’ expresses a critical commitment to democratic values of popular participation and political equality, allied to an urgent imperative for theorists to articulate and analyse new solutions to the problems of democracy’ (ibid.: 4). By offering a broad interpretation of innovation, Saward invites us to examine novel perspectives of politics and democracy. Rather than a fixed set of practices, emphasis was put on the ongoing effort to develop both the theoretical ideals and practical forms of democracy. Innovative forms of deliberation, representation, and association should therefore facilitate the expansion of democracy in response to current challenges. In an attempt to cement the concept through knowledge of real-life experiments, Smith defined DIs as the ‘institutions that have been specifically designed to increase and deepen citizen participation in the political decision-making process’ (ibid.: 1). By doing so, DIs emphasise the inclusion of citizens in decision-making processes related to policy and law-making, through mechanisms such as popular assemblies, mini-publics, participatory budgeting, direct legislation, and e-democracy…(More)”