Stefaan Verhulst
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)”.
Paper by Ofentse Tshiamo Mosala and Sibonelo Mpanza: “The incorporation of artificial intelligence (AI) within public administration (PA) marks a profound transformation of traditional governance. It carries the potential to enhance institutional efficiency, enable data-informed decision-making, and deepen citizen engagement. Drawing on secondary sources from leading peer-reviewed publications and policy reports, this study integrates the global trajectory of AI adoption in public governance. A narrative review, complemented by a bibliometric scan of prior work, identifies how AI applications manifest across three administrative spheres: internal operations, service delivery, and policymaking. The findings reveal significant opportunities, including the automation of administrative routines such as fraud detection through machine learning, the use of predictive analytics and conversational agents for citizen services, and data-driven policymaking informed by large-scale information systems. Yet, implementation remains burdened with institutional and ethical barriers, algorithmic bias, data privacy risks, and bureaucratic resistance to change being most prominent…(More)”.
Paper by Elizabeth G. Campolongo et al: “Artificial intelligence (AI) technologies and their capabilities have advanced rapidly over the past few years, leading to widespread interest and adoption in the biological and ecological sciences. However, the data infrastructure and computational resources that provide the foundation for AI workflows in ecology and biodiversity science remain fragmented. Moreover, the flow of value remains largely unidirectional: data move from collectors, through repositories and aggregators into models, and finally to research products or management actions. The derived value, such as corrected taxonomies, annotations, quality flags, and insights, rarely returns to source communities. We propose an extension of the original FAIR principles to address the needs of AI-ready infrastructure: the AI-FAIR metadata criteria, which place AI-enabled science in the context of a bidirectional Question–Data–Model triangle. This framework is designed to facilitate large-scale content-based analysis across diverse collections and ensure that the scientists generating the data benefit from the AI-enabled advances built on their data. Alongside these criteria, we introduce an agentic evaluation workflow 1 that both rates existing datasets on the AI-FAIR and traditional FAIR criteria and provides actionable feedback where critical metadata is missing or incomplete. This modular dataset evaluation tool, paired with a domain-appropriate checklist, translates these principles into a reusable assessment of machine-learning readiness, AI-readiness for specified tasks, traceability, and ethical and CARE-aligned stewardship. Through this approach we aim to create a community-informed system to close the model data loop, so that AI also enriches data to further enable scientific discovery…(More)” (See also Moving Toward the FAIR-R principles: Advancing AI-Ready Data).
Essay by Grace Huckins: “In 2001, a decade after he invented the World Wide Web, Tim Berners-Lee articulated a new vision. Together with two colleagues, he published an article in the magazine Scientific American in which he outlined what he called the “Semantic Web”: an internet designed to be navigated by autonomous software agents that could address our every need, without requiring us to muck about with browsers and search engines ourselves.
To illustrate what this might look like, Berners-Lee and his colleagues told a story about a pair of siblings who use their agents to automatically research and book physical therapy appointments for their mother, making sure they would be covered by insurance and fit their busy calendars.
At the turn of the millennium, this vision seemed far-fetched: Google had only been online for a couple of years, and most people were still on dial-up connections. But a quarter-century on, the dream of autonomous software agents that use the internet and carry out tasks for us has finally been realized. New tools such as Meta’s blockbuster Muse agent, OpenAI’s Dots, and Anthropic’s Claude Duet are being given access to people’s email inboxes and bank accounts, Meanwhile, in a series of worrying incidents, agents created by OpenAI broke free of their sandboxes and caused havoc online.
Just as the rise of smartphones sparked the development of a more atomized and attention-grabbing internet, the advent of powerful AI systems heralds a new era: We’re facing not the semantic web but the agentic web. This new web is developing out of sight and in ways that might not be entirely within our control. As it grows, the agentic web will inevitably transform the human web—and whether the human web survives this transition is anyone’s guess…(More)”.
Article by Ann Borda and Bastian Greshake Tzovaras: “In this perspectives article, the authors review key factors generating problematic oversight and participatory data governance gaps around AI (artificial intelligence) and machine learning (ML) applications and the role of crowdsourced citizen science in addressing these gaps. Within this context, there are challenges in a broader AI ecosystem of power asymmetries lacking participative governance of data use by commercial providers and in unregulated commodification of citizen science data. Such challenges potentially can diminish the effectiveness of crowdsourced citizen science approaches in the participatory data governance of AI/ML applications. Notwithstanding, crowdsourced citizen science has a keystone role in its ethical grounding in communities, and this study proposes consideration of crowdsourced citizen science as contributors to public interest policy and alternative models of collective data governance operationalization. These models can potentially support crowdsourced citizen science as co-partners through multistakeholder processes, such as public–private partnerships, across the AI/ML application lifecycle. A normative framework for collective data governance coalesces perspectives on emerging models, such as data commons and data cooperatives, and repositions the notion of the ‘collective’ moving away from a wholly data-centric approach. Future research is required to address specific collective data governance risks, such as recentralizing control despite participatory paradigms. Although these risks might be mitigated in evolving collective data governance partnerships focused on public good, these are yet to be tested more rigorously and, thus far, partnerships may only be practicable if there is a willing alignment and collective demonstration of societal benefits in an equally rapidly emerging monopolistic and solutionism-directed AI/ML ecosystem…(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
Article by John M. Kamensky: “…A new report by the IBM Center for The Business of Government documents that 21st century citizens are performing a new role as they engage with their government at all levels – they are becoming innovators. The study’s authors, University of Wisconsin professors Satish and Priya Nambisan, say that a trifecta of austerity, complexity, and digital technology has created this new opportunity for everyday citizens to help “co-create” new solutions for solving civic problems in their communities.
The term “co-creation” refers to the development of new public services by citizens in partnership with government. The authors outline four roles that citizen co-creators often assume: explorer, ideator, designer, and diffuser. According to the authors:
“As explorer, citizens can identify, discover, and define emerging and existing problems in public services. For example, the New York-based Datakind initiative involves citizen volunteers using their data analysis skills to mine public data in health, education, environment, and more areas to identify important civic issues and problems.
“As ideator, citizens can conceptualize novel solutions to well-defined problems in public services. For example, initiatives such as federal government’s Challenge.gov and OpenIDEO employ online contests and competitions to solicit innovative ideas to solve important civic problems.
“As designer, citizens can design and/or develop implementable solutions to well-defined problems in public services. For example, as part of initiatives such as NYC Big Apps and Apps for Californians, citizens have designed mobile apps to address specific issues such as public parking availability, public transport delays, and more.
“As diffuser, citizens can directly support or facilitate the adoption and diffusion of public service innovations and solutions among well-defined target populations. For example, physicians interacting with peers in dedicated online communities have assisted government agencies in diffusing health technology innovations.”..(More)”