Stefaan Verhulst
Book edited by Abbey K. Elder, Shannon M. Smith and Bryan Ricupero: “Open movements all center public access and broad representation and work to prevent the concentration of power governing information and its monetization. As general discussion of openness has narrowed to focus on areas like open science, data, access, and education, these individual communities have grown distanced from each other and, occasionally, their shared values.
In five sections, Open Movements: Recognizing Challenges and Building Connections aims to surface the challenges in each of these movements and how we might more effectively collaborate for sustainable change:
- Forging and Maintaining Open Communities
- Exploring the Structures that Support Open
- Improving Access Points for Open
- Navigating Choice and Engagement in Open
- Reflecting on Our Past to Build an Open Future
Open is a continually evolving environment, but the values that guide our systems and practices will be critical well into the future. Open Movements will help practitioners engage more thoughtfully in work that supports not just broad access to information but also public empowerment. This book is also available in an open access edition…(More)”.
Book by George Rzevski: “The book describes, using clear and concise language, the next generation of Artificial Intelligence (AI), which consists of swarms of hundreds of thousands of intelligent digital agents that exhibit collective intelligence greater than the sum of the intelligence of individual agents. These agents have access to domain knowledge stored in the attached ontology and do not need training to operate. Swarms of agents self-organise to adapt to environmental changes, resist hacking, and coevolve with the systems they interact with.
The author explains the basics of Swarm AI and its uses, including adaptive schedulers, pattern recognisers, semantic processors, variable geometry engineering products, and teams of space robots. They show how swarms of agents can handle operational tasks in businesses and protect nations from pandemics, financial crises, and military attacks.
The book is written for curious readers who want to understand the simple principles behind the power of the next generation of AI. It is also aimed at business executives, administrators, politicians, government leaders, regulators and policy makers, offering them insights into the new tools that will transform the organisations they lead and the world they inhabit…(More)”.
Article by Marianne Gros: “The European Commission is now using artificial intelligence to draft new laws and simplify existing ones, it said in an annual review of progress on its ongoing simplification drive.
As part of a major plan to cut red tape and administrative costs for businesses, the EU is making deep reforms to how it creates and enforces rules on everything from tech, agriculture and migration to environmental protection and taxation.
To do that, the Commission is deploying new digital tools internally to “modernise” lawmaking. The tools aim to help its staff “identify similarities across legal acts, pointing at potential overlaps, inconsistencies or duplicates, as well as opportunities for simplification,” it said in an annual simplification report presented on Wednesday.
According to the document, the Commission is also beefing up legal training and staff to make sure new rules are not overly complex.
“Our simplification agenda is more than figures in a report. It is a story unfolding every day in the offices, factories and farms across Europe,” said EU Simplification Commissioner Valdis Dombrovskis at a press conference. Small business owners are spending “less time filling in forms and more time focused on innovation and expansion,” he added.
The EU’s rulebook has been widely criticized for being overly complex and, at times, inconsistent. The Commission’s simplification drive is designed to fix this, making it easier for businesses and governments to comply with its laws by reviewing old legislation and adopting digital tools…(More)”.
Article by John Palfrey: “Sometime in the 18th century, the Enlightenment settled a contentious, centuries-old question: Where does knowledge come from? For millennia, many people had pointed to God. But the early modern period brought a new consensus, namely that knowledge comes from human scientists working at the edge of what is known, armed with observation, mathematics, experimentation, and logic.
As the historian Steven Shapin has noted, this was a social as much as an intellectual revolution. Science started rewiring our shared value system around the idea that research and rigor are virtuous. We held ourselves and our highest institutions to the standard of scientific inquiry.
No political system reflects this shift better than democracy. The ideal of popular sovereignty rests on the principle that each of us is capable of comparing candidates or policy proposals and reaching our own informed conclusion.
Today, we are on the cusp of a similar revolution. In September, one of OpenAI’s models took less than a week to solve a math problem that had stumped scientists for decades; and AI systems are already identifying novel antibiotic candidates by searching chemical spaces too vast for any research team to explore. In a growing number of fields, machines are autonomously producing new information. Humans are still posing the questions and evaluating the outputs, but the generative act—the creative leap across the unknown—is increasingly sparked by systems that do not think the way we do or really think at all.
As we saw with the Scientific Revolution, new information systems often lead to new epistemological and ethical systems. AI implicitly teaches us to value ease, speed, and outsourcing, and these values do have their place in certain contexts. No human can parse as much data as quickly as an AI can, and in sectors like medicine, speed often saves lives.
But as anyone who has watched the internet evolve in recent years knows, moving fast can mean breaking things. We cannot allow our democratic institutions to become one of those things…(More)”.
Paper by Haiwen Li et al: “As AI systems increasingly mediate human communication, identifying conditions under which they augment collective sense-making is a critical challenge. Through a randomized experiment (N=1,173 U.S. participants across 391 small-group deliberations), we investigate whether AI-guided self-reflection improves subsequent deliberative discourse. We show that engaging with a non-directive AI chatbot instructed to structure self-reflection prior to group deliberation yields significantly higher deliberation quality (measured by the Discourse Quality Index, DQI) compared to a baseline of no self-reflection (Cohen’s d=0.31) and a condition of unassisted self-reflection (d=0.27), and the positive impact is most pronounced in groups with disagreement. This improvement may be driven by more sophisticated argument justifications, greater consensus-building effort, and increased respect for counterarguments. Furthermore, after self-reflection, participants in the AI condition reported higher empathy toward dissenting peers and a deeper understanding of opposing reasoning. Beyond observed behavior, our findings are empirically relevant to open debates between political philosophy and psychology surrounding self-reflection, demonstrating that interactive AI could transform solitary thinking into productive internal reflection. Together, these results demonstrate that even simple AI-based reflective scaffolds can foster more productive group dynamics, opening new avenues for leveraging AI to augment collective intelligence…(More)”.
Essay by Stefaan Verhulst: “…Data openness remains desirable, yet in the age of AI it can also be weaponized. Below, I propose a framework that moves beyond the open-versus-closed binary, complements the FAIR principles, and advances AI-ready data governance through five dimensions of access: availability, accessibility, accommodation, affordability, and acceptability, while clarifying who is responsible for aligning them.
…In 1981, Roy Penchansky and J. William Thomas argued that access to health care is not a property of a clinic or a patient but of the fit between them. They broke that fit into five dimensions: availability, accessibility, accommodation, affordability, and acceptability. A service could exist, be nearby, and be free and still be inaccessible if it was organized around the provider rather than the patient or if the community did not trust it.
That insight has since been carried over to public health data, where the five A’s are used to show what is lost when a common dataset is discontinued. In what follows, I argue that the five A’s deserve a wider application. In particular, they can help us navigate a debate about data and AI that still treats access largely as a door: open or closed, licensed or not, scraped or blocked. Penchansky and Thomas remind us that access is a relationship, and relationships have to be negotiated and maintained.
AI changes that relationship in two ways. First, it adds a new kind of user: models and agents that consume data at a scale and speed no human analyst ever did. Second, it changes the stakes. As I have argued elsewhere, openness has been weaponised, and the defensive response is a data winter in which data have become more enclosed and more scarce. Seen through the five A’s, the data winter is not one failure but five, and they feed on each other. Each also points to a specific task that, today, too often belongs to no one. That task is the work of the data steward.
Reading the five A’s through the fourth wave of open data
In the below, I examine the five A’s through the lens of data for AI, or what we have called the fourth wave of open data. The first wave was driven by freedom of information laws, the second by open-by-default portals, and the third by purpose-driven reuse and data collaboratives. The fourth asks how data can be prepared for generative AI and how generative AI can in turn make data more usable.
That second direction (AI for data) adds nuance to the framework. AI is not only a new consumer of data that puts pressure on each of the five A’s; it is also a tool for improving them. It can help users discover datasets, generate and enrich metadata and documentation, and make access conversational so that people can ask questions of data in plain language instead of navigating portals and schemas…(More)”.
Table: The five A’s of data access in a time of AI
Book by Alice Sullivan: “… investigates one of the most contentious and puzzling social phenomena of our time: the removal of sex from data, social policy and public discourse.
Sociologist Alice Sullivan traces the origins of the conflation of sex and gender in sexology, postmodern queer theory, and transgender rights activism. She analyses the way these ideas moved beyond academia and permeated our institutions, tracing the consequences everywhere from statistics bureaux to sports bodies to prisons to the effects on children and young people. Along the way, she exposes the pressures—political, institutional, and social—that have discouraged open debate and sidelined evidence in favour of orthodoxy.
Sex, Lies and Statistics explains why we are losing data on sex, why we still need it as much as ever, and how we can move forward by recognising the distinction between sex and transgender identities. Clear, accessible, and deeply researched, this book makes a powerful case for evidence-based policymaking and honest public discussion. At a time when trust in institutions is increasingly fragile, Sullivan argues that if we are to avoid a post-truth culture, we must reject tribalism and groupthink, welcome critical voices, and reclaim the virtue of tolerance…(More)”.
Article by Martin Kahanec, Naomi Ellemers, Adam Izdebski, Rafal M. Łukasik, Dimitra Simeonidou, Rémy Slama and Mangala Srinivas: “For scientists, thoughts about democracy can feel distant from their work. Yet, around the world, governments are limiting academic freedom and interfering with decisions about what research is funded and who performs it. Some scientists are also facing politically motivated interference and attacks. As members of the European Commission’s Group of Chief Scientific Advisors, we feel it is urgent to address this concern and that we have a responsibility to make scientists’ voices heard.
Science and democracy are often viewed as distinct — science as a source of discovery, expertise and innovation and democracy as a set of institutions, rules and procedures. But, in reality, the two are intertwined.
Science is an essential backbone of democracy. In an increasingly complex and fast-changing world, people need access to verified information and independent knowledge to make informed decisions. Research aids that by generating reliable evidence and fostering critical enquiry, thus enabling a plurality of views to be discussed and creating conditions in which societies can learn from experience.
In May, Ekaterina Zaharieva, the European Commissioner for Startups, Research and Innovation, asked us to provide advice on how science can strengthen the European Union’s democratic functioning and resilience.
Independently of that process, here we put forward three pillars through which science enables democracy: establishing shared factual baselines, aiding trust in institutions and providing tools to adapt policies and organizations in response to changing circumstances…(More)”.
Article by Moshe Y. Vardi: “Peggy Noonan is a weekly columnist for The Wall Street Journal. I often disagree with her opinions, but I find her to be one of the most astute observers of the American social-economical-political zeitgeist. When she described in 2018 Silicon Valley executives as “moral Martians who operate on some weird new postmodern ethical wavelength,” I realized that the popular view of Silicon Valley as a fountain of technical innovation and cool gadgets had changed. Indeed, in my January 2019 column, I also quoted Niall Ferguson, a Hoover Institution historian, who described cyberspace as “cyberia, a dark and lawless realm where malevolent actors range.” The Economist magazine coined the term Techlash to describe what was then a new phenomenon of growing hostility toward the tech giants. The COVID pandemic distracted us from the techlash of the late 2010s. In fact, technology was our salvation during those dark days of social distancing. But now, the techlash is back!
In her Sept. 10, 2026 column “Pause AI for Humanity’s Sake,” Noonan argued, “When something speeds up and gets out of control, it isn’t alarmist to demand prudence and deliberation.” This is not the first call to slow down AI development. “Pause Giant AI Experiments: An Open Letter” was a letter published by the Future of Life Institute in March 2023. But The Wall Street Journal is, in essence, the flagship publication of the U.S. business community; the publication objects practically always to government regulation. A day earlier, Paul Tudor Jones, another columnist at The Wall Street Journal, warned that “AI May Become the Third Superpower”, and called for U.S. president Donald Trump and China president Xi Jingping to “develop a shared strategy to confront it.” As reported in the 2026 AI Index, U.S. sentiment has turned negative on AI, with the majority concerned rather than optimistic about AI’s societal impact…(More)”.
Article by Denice Ross & Melanie Klein: “From the small businesses relying on census figures to a cancer patient who survives because they found the right clinical trial, federal data are the essential, invisible infrastructure of modern American life. The data enterprise of the United States government is world-class. But it’s not perfect, and American communities have long needed data that are more detailed, timely, and relevant while also protecting privacy and security.
Unfortunately, federal data are held together with a patchwork of outdated and brittle policies that leave our data vulnerable, and currently, federal data programs face a perfect storm of funding cuts, personnel departures, and the erosion of institutional knowledge.
The solution is not to simply rebuild the data infrastructure as it was. To build a more healthy, safe, prosperous and equitable society, we need a new approach to data. That means catalyzing the field of data policy.
More robust public data policies will provide communities with better warnings for extreme weather, protect people from illness and disease, give businesses market insights they need to be profitable, and enable innovations like autonomous (“captainless”) shipping and AI-informed emergency response. Such policies might include more robust engagement to enhance the utility of federal data, from strengthening funding and talent pipelines to sustain and improve the federal data ecosystem, to modernized privacy and data sharing to improve public trust and deliver better services to the American people…(More)”.