Stefaan Verhulst
Article by Andrew Stokols: “In April 2025, twenty-one humanoid robots lined up beside human runners in Beijing’s Yizhuang district for what organizers billed as the world’s first humanoid half marathon.1 Most didn’t finish. Several fell over at the start. The coverage abroad ranged from hyperbolic to dismissive: evidence of Chinese technological mastery or a performative spectacle designed to impress the world.
China’s technology landscape is indeed layered with infrastructures and buildings built primarily to be seen: exhibition halls, demonstration zones, nighttime drone shows, smart-city command centers with wall-sized screens. Chinese has a term for the tendency, 面子工程 (mianzi gongcheng), “face engineering,” and in 2025 Xi Jinping himself criticized wasteful vanity projects.2 But to stop there misses what these projects do. A robot race on a closed urban course is also a test: of locomotion over real pavement, of battery endurance, of how machines behave among crowds. Yizhuang, not coincidentally, is also where Beijing’s autonomous vehicle testing zone has spent five years accumulating edge cases on public roads.
Chinese policymakers have a word for what connects these things, and it has become one of the most important concepts in the country’s industrial policy for artificial intelligence: 场景, changjing, or “scenario.”
In Chinese policy vocabulary, a scenario is a bounded, real-world environment in which an emerging technology can be deployed, observed, and improved before it is commercially viable. “Scenario innovation” (场景创新) and “scenario opening” (场景开放) entered official usage in the late 2010s, and in July 2022 six central agencies, led by the Ministry of Science and Technology and including the Ministry of Industry and Information Technology, issued the Guiding Opinions on Accelerating Scenario Innovation to Promote High-Quality Economic Development through High-Level AI Application..(More)”.
Paper by Alan Chan et al: “In contrast to even a year ago, AI systems now write most of the code inside the companies that build them. As more of the AI research and development (R&D) pipeline is automated, could AI progress radically accelerate in an “intelligence explosion,” where years of advances are compressed into months or less? Preliminary evidence suggests that it could. In this work, we assess this evidence, analyze an intelligence explosion’s potential impacts, and propose policy responses. AI systems are on track to automate most AI R&D work within a few years, and possibly all of it. If this triggers an intelligence explosion, it could dramatically bring forward AI’s benefits, but also pose extreme risks: capabilities growth could accelerate far beyond what society can keep up with, humanity could lose control over superhuman AI systems, and checks on power within and between states, companies, and branches of government could be severely eroded. Although there remains much uncertainty about these possibilities, the high stakes warrant serious further attention. Policymakers should urgently obtain more visibility into the automation of AI R&D, develop ways to steer and constrain an intelligence explosion, and prepare society to adapt to an intelligence explosion’s impacts…(More)”.
Press Release: “The U.S. National Science Foundation announced today the launch of the NSF Open Knowledge Network (NSF OKN), a national open-data infrastructure that links 43 interconnected knowledge graphs and tens of billions of connected facts across health, the environment, justice, manufacturing, and national security. The network is live and open to the public at okn.us.
NSF OKN provides a structured, persistent, verifiable knowledge layer to complement contemporary artificial intelligence models, providing grounding of facts, attribution, and knowledge governance essential to critical AI applications in a number of fields. OKN’s value goes beyond machines querying structured knowledge: Its deeper value provides AI systems with a shared, explicit, interoperable, and auditable representation of the world on which they are expected to reason and act…(More)”.
Paper by Maojun Sun et al: “In recent years, data science agents powered by Large Language Models (LLMs), known as “data agents,” have shown significant potential to transform the traditional data analysis paradigm. This survey provides an overview of the evolution, capabilities, and applications of LLM-based data agents, highlighting their role in simplifying complex data tasks and lowering the entry barrier for users without related expertise. We explore current trends in the design of LLM-based frameworks, detailing essential features such as planning, reasoning, reflection, multi-agent collaboration, user interface, knowledge integration, and system design, which enable agents to address data-centric problems with minimal human intervention. Furthermore, we analyze several case studies to demonstrate the practical applications of various data agents in real-world scenarios. Finally, we identify key challenges and propose future research directions to advance the development of data agents into intelligent statistical analysis software…(More)”.
Paper by Katja Mayer: “Open Science has long been framed as a normative project grounded in values of transparency, accessibility and collaboration. Yet in the age of generative AI, many research data practitioners, who once championed openness, are increasingly questioning how these commitments can be upheld when openness also enables undesired large-scale data extraction, decontextualization and enclosure. This paper examines how the normativities of openness are being re-negotiated, and how researchers navigate the ethical and political tensions that arise when their work is repurposed for proprietary AI systems.
Drawing on in-depth qualitative interviews, I explore how situated ethical reasoning informs decisions about data sharing. Participants described new risks and dilemmas, from uncontrolled scraping and heightened re-identification threats to the reframing of FAIR (Findable, Accessible, Interoperable, Reusable) principles as pipelines for AI-readiness. Their accounts reveal openness as a relational and contested practice, embedded in specific institutional, infrastructural and community contexts, and continuously re-balanced between care, accountability and collaboration.
Rather than rejecting openness outright, interviewees described recalibrating it – through selective sharing, alternative infrastructures and advocacy for commons-based governance models…(More)”.
Report by the World Economic Forum: ‘Data sovereignty has become one of the defining governance questions of the digital age, yet the concept remains contested. Governments, individuals, indigenous groups, technology companies and regional blocs all define it differently, and localization alone cannot deliver it. Sovereignty in practice means aligning legal authority with technical control, across storage, compute, models and identity, while still enabling trusted, equitable collaboration across borders.
This briefing paper explores how governments, businesses and civil society can build and verify credible data sovereignty, balancing legitimate control with the trusted interdependence most actors need to thrive…(More)”.
Article by Quentin H. Riser et al: “Fragmentation across early care and education (ECE) systems in the United States obscures how many children access services and how these experiences shape school readiness. This study leverages Iowa’s Integrated Data System for Decision-Making (I2D2), which links health, social determinants of health (SDOH), and education records, to provide one of the first unduplicated counts of ECE participation statewide. Drawing on a matched cohort of 27,321 children eligible for kindergarten in 2017–2018, we examine three aims: (1) describe patterns of centre-based ECE participation; (2) assess associations between child/family characteristics and participation; and (3) evaluate links between ECE and kindergarten suspensions and attendance. Results indicate that 73\% of children participated in at least one ECE program prior to school entry, with substantial overlap across public and private preschool, and subsidised or unsubsidised child care. ECE participation varied as a function of poverty, race/ethnicity, maternal education, and cumulative early-life risks. ECE participation was associated with better kindergarten attendance but not suspension. Findings underscore both the promise of integrated data for advancing equity-driven research and the need for policies that target families with the greatest barriers to ECE access…(More)”.
Article by Katherine Loh, and Imad Aad: “On September 28, 2025, Swiss voters once again considered a referendum on a national digital identity (e-ID) law passed by Parliament. The government’s first attempt at legislating a national digital ID passed in 2019 but was roundly rejected by referendum in 2021, when 64% of voters opposed the law, citing concerns that it delegated operations to private companies. The popular backlash catalyzed years of national deliberation on the question of what kind of e-ID model could meet citizens’ demands for transparency, privacy, and security. Parliament passed a redesigned e-ID law in 2024, and this one stood the test of another referendum, but by the thinnest of margins. The new law was supported by just 50.4% of voters.
This referendum back-and-forth is not unfamiliar to the Swiss. By some accounts, Switzerland holds one-fifth of the world’s national referendums. Switzerland’s direct democracy enables any party that gathers more than 50,000 signatures to launch a nationwide referendum. Combining national, cantonal, and municipal-level referendum initiatives, Swiss citizens vote on around a dozen referendums a year.
What was unique about the e-ID debate was the way it provoked an unusual level of anxiety and mistrust. In a country where people have long relied on the digital world for banking, payments, communications, and travel planning, development of a national digital ID system brought into sharper focus a broader unease around the digitalization of society. For the Swiss, an e-ID represents more than an app; it is a bellwether for the future of long-cherished rights and values around identity and citizenship in the digital age…(More)”.
Paper by Stefaan Verhulst, Adam Zable, Begoña Gonzalez Otero and Jacqueline Lu: “Contemporary data governance has converged on participation. Citizens’ assemblies, deliberative polls, co-design workshops, and community advisory panels are now routinely suggested to build trust and secure a social license for the re-use of data. Yet this consensus rests on an unexamined assumption: that giving affected communities a voice will, on its own, change what data holders do. This paper argues that the field needs to complement voice with leverage-the capacity of individuals and communities to make their participation consequential by attaching credible legal, institutional, contractual, economic, or reputational costs to being ignored. Drawing on labor-relations bargaining theory, the sociology of disputes, and James C. Scott’s account of legibility, we develop leverage as an analytic category defined by three properties: it is counterfactual, enforceable, and durable. We further argue that leverage presupposes epistemic legibility-affected publics can rarely impose costs on a system they cannot see, document, or contest-and that most existing transparency instruments equip regulators and procurers rather than the governed. We then map six sources from which communities and data subjects can derive leverage (legal and regulatory; institutional decision; contractual; economic; data access; and political and reputational), examine the intermediary structures that aggregate and sustain it, and propose a six-dimension evaluative framework-obligation, enforceability, consequence, monitoring equipment, revocability, and institutional durability-for auditing whether any social license process actually redistributes power. The paper’s central claim is that a social license worth the name must share the properties of a legal license: conditions, a licensor, and enforceable remedies for breach…(More)”.
Report by UN Women: “…provides practical guidance for using data to design, implement and monitor gender-responsive care systems.
The publication is grounded in the 5Rs framework—Recognize, Reduce, Redistribute, Reward and Represent. It shows how governments and partners can connect diverse data sources, institutions and stakeholders to better understand care needs, address inequalities and translate evidence into more inclusive policies, services and investments across Asia and the Pacific…(More)”.