Stefaan Verhulst
Project by AidData: “… has compiled an inventory of key development data assets, which will feed into future work to track funding to this data ecosystem.
Our interactive Data Asset Inventory provides granular detail on 496 assets published by 230 organizations across 7 of the most important sectors for development: agriculture and food security, disaster response, education, environment, gender, governance, and health.
Explore the public beta, and share your feedback to inform the next phase of this effort. Are there important data assets you work with that are missing from our inventory?…(More)”.
Report by Tessa Dunlop, Ventseslav Kozarev, Ângela Guimarães Pereira and Paulo Rosa: “Citizen engagement processes can be transformative, especially for participating citizens. But what is their impact on policymaking, democratic systems and the broader public? This report maps a range of impacts across policy, institutions and society. Qualitative interview and survey data reveal that public administrations that engaged in citizen participation saw significant benefits, including improved policymaking, stronger democracies, greater legitimacy and increased trust in government, so much so that they are ready to conduct other participatory exercises. The study suggests and illustrates a number of indirect impact pathways, indicating that the extent of policy impact may be underestimated. Furthermore, citizen engagement drives culture change within institutions and enhances their capacity to engage more directly with citizens. Looking through a relational lens, citizen engagement processes foster closer connections between citizens and policymaking, but also between stakeholders, civil society, citizens, policymakers, and others. Key factors include the commitment of institutions and policymakers, and public communication about citizen engagement processes. Overall, the key message is that desired impact should be the starting point of citizen engagement processes, not an afterthought – support may erode if citizens and policymakers cannot see tangible outcomes. Commissioning bodies and organisers should prioritise commitment, legitimacy, and integration to ensure meaningful impacts…(More)”.
Book by Marion K. Poetz and Henry Sauermann: “…explores how millions of people can significantly contribute to scientific research with their effort and experience, even if they are not working at scientific institutions and may not have formal scientific training.
Drawing on a strong foundation of scholarship on crowd involvement, this book helps researchers recognize and understand the benefits and challenges of crowd involvement across key stages of the scientific process. Designed as a practical toolkit, it enables scientists to critically assess the potential of crowd participation, determine when it can be most effective, and implement it to achieve meaningful scientific and societal outcomes.
The book also discusses how recent developments in artificial intelligence (AI) shape the role of crowds in scientific research and can enhance the effectiveness of crowd science projects…(More)”
Paper by Michael J. Mauboussin and Dan Callahan: “In 1932, Bernard Baruch, a wealthy financier who made his fortune on Wall Street in the early 20th century, contributed the foreword to a reprint of the 1852 edition of Charles Mackay’s classic book on markets, Memoirs of Extraordinary Popular Delusions and the Madness of Crowds. Invoking a dictum from Friedrich von Schiller, a German poet and philosopher, Baruch wrote: “Anyone taken as an individual, is tolerably sensible and reasonable—as a member of a crowd, heat once becomes a blockhead.” He added, “Without due recognition of crowd-thinking (which often seems crowdmadness) our theories of economics leave much to be desired.” About 40 years later, Eugene Fama, a professor of finance at the University of Chicago and a winner of the Nobel Prize in Economics, published “Efficient Capital Markets: A Review of Theory and Empirical Work.” It is among the most famous papers ever written in finance. This might be considered the theory Baruch had in mind. Fama posited, “A market in which prices always ‘fully reflect’ available information is called ‘efficient.’” Fama found that strategies investors commonly applied to try to outperform the market, including using past price patterns to project the future and doing fundamental analysis to distinguish between price and value, failed in their objective. In other words, there is no reliable way to take advantage of the blockheads. Nearly all those who study markets carefully agree that they appear sensible for the most part, as theory would have it, but periodically go bonkers. Having one framework to accommodate both realities is useful. James Surowiecki wrote about such an approach in 2004. Riffing on Mackay’s madness of crowds, Surowiecki called his book, The Wisdom of Crowds. He showed that crowds can be remarkably accurate in reflecting objective values or outcomes. Indeed, the prices generated by collectives commonly converge on the proper theoretical price in experimental settings…(More)”
Paper by Weston Anderson et al: “Artificial intelligence (AI) and machine learning (ML) methods offer substantial promise for monitoring and predicting acute food insecurity when paired with domain experts as part of a trusted and accountable system. However, using AI/ML-based methods may cause costly, dangerous mistakes if implemented uncritically. Funding for humanitarian aid has been drastically cut, putting tremendous pressure on food security early warning systems to use AI as a means of cutting costs. In this Comment, we outline where AI/ML methods offer promise to make early warning systems more adaptable and effective as well as where the use of AI/ML is ill advised. We recommend that AI/ML be used to improve monitoring and forecast models in the data-rich portions of food security early warning systems, such as those that rely on climate models and remote sensing. Where data are irregular and sources are varied, AI/ML should instead be used to improve the accessibility and timeliness of socioeconomic data collation. AI can augment food security analyst capabilities, but an analyst is needed to maintain clear systems of accountability and review for all issued forecasts…(More)”
Article by Nathan Darmon and Tom Reed: “Imagine the following scenario. A chemical manufacturer relies on Anthropic’s Claude to generate the groundwater reports submitted each quarter to regulators. Over months of routine work, Claude pieces together that the figures are being systematically doctored, and that the aquifer supplying the nearby town has been contaminated for years. The model raises this concern with the manufacturer, who waves it off repeatedly.
What should Claude do? It could comply under protest, maximizing user autonomy while potentially endangering the public; it could refuse to file the documents, frustrating a user who could carry out his schemes elsewhere; or it could go one step further, alerting regulators and warning townspeople directly at the risk of becoming tyrannically paternalistic.
To make this judgement call, Claude refers back to the foundational guidelines enshrined in its Constitution—a “detailed document describing Anthropic’s intentions for Claude’s values and behavior.” In it lie 80 pages of moral philosophy describing the virtues and character traits Anthropic would like its model to embody. OpenAI has published its own variant, and both Microsoft and Google DeepMind are rumored to be drafting theirs as well.
Yet these constitutional instructions remain relatively vague. They include statements such as “Claude can reserve independent action for cases where the evidence is overwhelming and the stakes are extremely high.” But what counts as “overwhelming evidence,” where does “extremely high stakes” begin, and what might “independent action” allow? Wrestling with hard questions of interpretation is inevitable.
When it makes these decisions, Claude is not alone. Labs string together a variety of internal teams to guide their models along, assembling what could best be called a “model-behavior production function”: the whole assembly line needed to get models to act according to a preferred philosophy. This process includes things like a “constitution team” making high-level rules and baking them into a model’s core through cycles of reinforcement learning, a “red team” stress-testing to see if these values hold under fire, some policy-specific teams making decisions on concrete issues like erotica or political speech, and a variety of product teams taking in user feedback.
Unfortunately, this process has some profound structural flaws. Writing high-level principles leads to ambiguities, conflicting principles, and open-ended interpretation with no set way of resolving them. The process is sporadic, and the coordination of multiple teams arguably creates unprincipled results. Users don’t know in advance how rules will be applied, which is just as useful as knowing the words that make up the First Amendment without knowing any subsequent precedents. Worse yet, there is no institutionalized mechanism to gradually refine rules over time, absent a major public backlash causing ad hoc revisions.
There’s a better way. Society has built an institution to solve these problems: courts. Courts apply open-ended rules, refine them over time, and clarify their meaning—all while providing coherence, adaptability, and greater transparency. The lesson for frontier labs is to build an internal court to shape how their models interpret ambiguous rules, and let its rulings build up into a kind of synthetic common law. A court-like mechanism is uniquely suited to the artificial intelligence (AI) context, far better than older attempts like Meta’s Oversight Board. Below, we provide a rough sketch for what this could look like…(More)”.
Paper by Haoxiang Guan et al: “Understanding the dynamic evolution of complex social phenomena requires both high-fidelity modeling of human behavior and large-scale simulations. Traditional agent-based models (ABMs) have been employed to study these dynamics, but are constrained by simplified agent behaviors. Recent advances in large language models (LLMs) enable agents to exhibit sophisticated social behaviors, yet face significant scaling challenges. We present Light Society, an agent-based simulation framework that advances both fronts. Light Society formalizes social processes as structured transitions of agent and environment states, governed by a set of LLM-powered simulation operations. Joint algorithmic and system optimizations, particularly a mixture-of-models engine that combines full LLMs with distilled surrogates, enable Light Society to efficiently simulate societies with over one billion agents. Grounded in real-world demographic profiles from the World Values Survey, simulations of Trust Games and opinion diffusion at up to one billion agents demonstrate Light Society’s high fidelity and efficiency in modeling diverse social phenomena, providing researchers with a practical foundation for hypothesis testing and the study of emergent collective behaviors at planetary scale…(More)”.
Paper by Amitava Sarder and Ranjan Kumar Mondal: “In the era of big data, social media platforms have become invaluable sources of information, offering vast amounts of data that can be analyzed to extract valuable insights. With the explosive growth of social media and the large volume of data it generates, noise has become a significant challenge in deriving meaningful insights. This article provides a comprehensive survey of noise elimination techniques in social media big data. In this paper, we review the literature on various noise elimination methods in social media big data and introduce a new classification of these strategies, such as preprocessing techniques, filtering methods, topic modelling, machine learning approaches, community detection, user-based filtering, outlier detection, anomaly detection, spam detection and related approaches. The survey examines the latest developments and recent advancements in noise reduction techniques, highlighting subcategories within each approach and their contributions to noise removal. Additionally, it discusses the challenges and opportunities associated with noise elimination in social media big data. This survey serves as a valuable resource for researchers and practitioners seeking an overview of noise reduction methods to improve the quality and reliability of social media big data analysis. Overall, this survey offers valuable insights into the landscape of noise elimination techniques in social media big data analysis, providing researchers and practitioners with a comprehensive understanding of existing methods and guiding future research in this important field…(More)”.
Report by Twilio: “…shows a stark perception gap in the public sector: while 88% of government organizations rate their citizen engagement as good or excellent, only 44% of citizens agree. The Connected Government Report (2026) shows that while public sector agencies are confident in their digital services, citizens report fewer tangible benefits from digital interactions than in previous years. The research explores critical priorities for public sector digital engagement as agencies transition from basic digitization to connected, meaningful communication.
The findings come as AI adoption in government continues to rapidly accelerate. The data shows 97% of public sector organizations have implemented at least one AI use case, and the average number of use cases has increased 50% since 2024. Additionally, while AI is expanding rapidly, few trust it, creating a critical confidence gap as agencies scale intelligent automation.
As part of the research, Twilio categorized public sector organizations into three maturity levels based on their level of conversational digital engagement, personalization, use of citizen data, and cross-department data sharing. These were broken down into three levels: beginners (22% of respondents), developing (50%), and leaders (28%). Digital leaders communicate in ways that are coordinated, personalized, and two-way, sharing data across departments and agencies. These leaders are also significantly more likely to use AI operationally to meet citizen needs…(More)”.
Paper by Hilke Schellmann et al: “Almost anything can now be generated with artificial intelligence: photographs, audio, video, documents, entire websites. As synthetic content becomes cheaper and more convincing, and as verifying what we see online grows harder, how do we sustain an information ecosystem in which facts can still be established and trusted? In this report, we identify the challenges brought to the information ecosystem by the increased accessibility of generative AI. We start from the premise that verification, authentication, and transparency are key pillars of online information integrity. We then describe how this emerging ecosystem challenges the trustworthiness of facts from the perspective of information producers, consumers, and intermediaries. Next, we turn to a discussion of potential interventions that can address the challenge of AI-mediated information integrity. These interventions are not exhaustive, but they represent the issues raised by participants drawn from a cross-section of journalists, researchers, and technologists who participated in an in-person workshop held at NYU’s Arthur L. Carter Journalism Institute in early June 2026. This report is intended to serve as a bridge across different actors in this complex information ecosystem: newsroom engineers, investigative and open-source reporters and editors, verification tool builders, independent journalists, policy makers, researchers, and media platforms. We aim to surface the primary challenges — and imagined interventions — to work towards a more resilient information future together”