Stefaan Verhulst

Article by Dylan Baysa, Melodie A. Lopez & Lana Fred:  “For years, nearly every piece of data the Tucson Indian Center (TIC) collected was designed to answer someone else’s questions. Since 1963, TIC has served the urban Native community in Tucson, Arizona, offering primary care and behavioral health services, diabetes prevention and wellness programs, employment and job training, and emergency housing assistance. Intake forms for those programs, satisfaction surveys, and reporting metrics were all developed to collect data that met federal grant requirements but told us almost nothing about what our community wanted to know about itself.

Eventually, those of us leading and working at TIC said, “Enough.” We began building a different kind of data system—one organized around our community’s priorities. But to understand why this work is both difficult and transformative requires understanding the origins of contemporary data systems that are used to quantify Native people in the United States.

For much of the 19th and 20th centuries, information about Native peoples was collected and controlled primarily by federal agencies, academic institutions, and other outside entities. Rooted in settler colonialism and white supremacy, that control was part of broader efforts to dominate Native land, identity, and governance, and it has left many Native nations dependent on data rarely collected by or for them. Data, including census records and blood-quantum classifications, were used to define Native identity, determine eligibility for land and government benefits, and advance assimilation policies, often with limited Tribal authority over how information was collected, interpreted, shared, or used. In this way, quantification became a tool of administration and control. Research also frequently took a deficit-based approach, emphasizing poverty, health disparities, unemployment, and other challenges while overlooking community strengths, cultural knowledge, Tribal priorities, and the social and historical conditions shaping those outcomes. In doing so, it reinforced stereotypes and informed policy without adequately reflecting Indigenous perspectives. Many of today’s data failures, such as the chronic failure to collect mortality data for Indigenous populations, continue that history through seemingly mundane administrative policy and practice…(More)”.

Paper by Yuhan Li,Nicolò Gozzi,Nicola Perra & Michele Tizzoni: “The concept of fairness has been extensively examined within the domains of Machine Learning and Artificial Intelligence more broadly. It remains, however, largely underexplored in the field of Computational Epidemiology. Considering the substantial influence that epidemic models exert on public health policy, particularly in the context of outbreak preparedness and response, this shortcoming is of great relevance. Here, we propose a mathematical framework, grounded in core principles from Social Epidemiology, for evaluating the fairness of computational epidemic models. We begin by applying our framework to a range of epidemic modeling approaches and simulation scenarios, such as the initial spread of COVID-19 in London, New York, and Santiago de Chile, as well as the 2016 Zika virus outbreak in Colombia, demonstrating its consistent capacity to assess model fairness across diverse disease dynamics. Subsequently, we illustrate how our definition of fairness can be incorporated into the design of immunization strategies as a way to enhance health equity while simultaneously improving the overall effectiveness of such interventions. Overall, our results offer a new systematic methodology for quantifying fairness in computational epidemiology…(More)”.

Article by Kyriaki Papageorgiou: “It is tempting to think of CERN only as a triumph of scale and engineering: a vast complex of accelerators, detectors, and tunnels built to probe the fundamental structure of matter. This image often stands in for “Big Science” more generally, reinforcing the idea that CERN’s primary lesson lies in technical ambition or concentrated resources. But this reading misses what has made CERN historically distinctive and politically durable. From its founding in 1954, in the aftermath of the Second World War, CERN was conceived not simply as a laboratory, but as a political infrastructure for rebuilding European science (and Europe) through cooperation. It was designed to pool resources and research capacity across the borders of its original 12 member states after decades of fragmentation and scientific brain drain. Its founding convention embedded a public, nonmilitary ethos from the outset, explicitly committing the organization to “have no concern with work for military requirements.”

CERN’s governance was treaty-based and intergovernmental, tying scientific collaboration to the shared funding commitments and collective decisionmaking of the CERN Council, through which member states vote to decide on scientific and administrative issues. Precisely because CERN made large-scale collaboration functional, productive, and credible, it has also become one of the most closely studied scientific infrastructures, offering unusually rich accounts of how trust is built in practice…(More)”.

Paper by Lauren A. Haygood and Todd Halihan: “More than 50% of Oklahoma’s surface water systems are classified as impaired by the US EPA, with limited funds designated toward surface water monitoring. Community science data can contribute to increasing the amount of quality-controlled government water quality reporting. A framework is presented for implementing a community-based approach to generating quality-controlled water field data to support water quality monitoring by local and regional governments. This research focuses on the design and implementation of a water quality community science project in Oklahoma, USA. In this study, community scientists collected water samples and produced over 100 measurements of key water quality parameters, including alkalinity, conductivity, total dissolved solids, temperature, and selected metals (e.g., iron, manganese, mercury, cadmium, and lead). The frequency and resolution of these community-generated data exceed those of conventional state government monitoring, demonstrating the potential for community science to provide cost-effective, high-frequency, and spatially expansive data sets, which fill monitoring gaps. Additionally, community science can act as a method to bridge the gap between stakeholders, scientists, and policy makers. This study highlights the quality-controlled methods, tools, and community-driven efforts shaping water quality research and public engagement in Oklahoma, a step forward in community-driven data acquisition and water policy…(More)”.

Paper by David D. Chen  et al: “Public health efforts addressing chronic disease and poverty require information that describes local conditions, preserves historical comparisons, and can be interpreted by the communities expected to act on it. Delaware’s My Healthy Community (MHC) platform brought health and social data together within a shared interface. Its 2025 decommissioning and transition to distributed sources offer a case for examining what community access to public data requires. We argue that access depends not on publication alone but on sustained stewardship and interpretive capacity. Adapting Penchansky and Thomas’s five dimensions of access (availability, accessibility, accommodation, affordability, and acceptability), we use Delaware examples, including a dated, topic-by-topic inventory, to show how access can erode even when data remain technically public. Substitute measures may not answer the original question, historical releases may become difficult to retrieve, and distributed sources require documentation and assistance. Free data also carry costs in staff time and expertise, and unexplained changes can weaken trust. Artificial intelligence makes analysis easier to produce without making it easier to verify, increasing the importance of these supports. We propose maintaining historical releases, documenting changes, and funding the partnerships and interpretive support required for responsible community use…(More)”.

Article by Thiago Moraes: “Since the adoption of the EU AI Act, which requires every EU Member State to establish at least one AI regulatory sandbox, such initiatives have been expanding across the EU and the wider European Economic Area (EEA). But how far have these initiatives progressed, and what can we learn from what has been made public so far? Here is a snapshot of the landscape as of October 2026.

The findings offer a snapshot of an evolving landscape. By the date of analysis, publicly available information pointed to completed AI Act’s sandbox pilots in Denmark, Germany, the Netherlands and Spain. The Spanish initiative stands out as a pioneer, with published statistics and reports and practical guidelines to support the AI Act implementation. Estonia, Latvia and Lithuania had launched pilots, while Sweden, Liechtenstein and Norway had publicly announced sandbox coordinators, but the reviewed information confirmed no sandbox pilots.

Table 1 summarises the findings across all 27 EU Member States and the three non-EU EEA countries. The four categories reflect the stage of development based on public available information at the time of the research…(More)”.

Table 1. Publicly documented state of play of AI Act regulatory sandboxes in the EU and EEA (October 2026)

Note: Data compiled from official public sources as of October 2026. 

Paper by Cuiyu Qin, L. G. Pee, Xiaojie Chen, and Xiaoguang Wang: “In the context of artificial intelligence (AI)-driven scientific research, research information practices are undergoing profound developments. This paper employs a mixed-methods approach, combining topic modeling and interpretive content analysis, to explore emerging research information practices associated with the integration of generative AI into research. This study identifies six significant shifts: from keyword-based search to prompt-based retrieval, from human sensemaking to delegated interpretation, from human innovation to algorithmic co-creation, from manual writing to generative authoring, from unregulated AI use to accountable disclosure, and from AI use to AI literacy. It further analyzes the implications of these emerging research information practices for research productivity, cognitive and critical thinking, academic integrity and ethics, and academic fairness. This study enriches existing research on information practices in evolving information environments and provides a practical path for researchers, libraries, publishers, universities, and research institutions to move beyond the simple use of AI toward the development of AI literacy…(More)”.

OECD Report: “Access to health data for secondary uses that serve the public interest can improve health outcomes, strengthen health system performance and support healthcare innovation. When combined with responsible and trustworthy AI, such data can generate new insights, enhance decision-making and help address complex health challenges. However, enabling access and re-use while safeguarding privacy requires robust governance and careful review by authorised bodies. Today, a lack of clear definitions and differing approaches across review bodies can create barriers to data access and impede collaboration within and across countries. This policy brief identifies five broad categories of criteria relevant to assessing public interest: population health and planning; research and innovation; ethical and risk considerations; health equity; and public communication and transparency. It also outlines approaches to help policymakers enable more consistent, trusted and effective health data governance…(More)”.

Paper by Mikel Asteinza, Hugo Viciana & Ivar R. Hannikainen: “Metaphors in science communication do more than simplify complex ideas; they impose certain epistemic frameworks with the potential to steer public attitudes and behavior. In this paper, we investigate the effects of metaphorical framing on public understanding and evaluation of de-extinction, a field where dominant metaphors (“resurrection,” “bringing back to life”) have been criticized for mischaracterizing the technology and fostering undue optimism. We presented a nationally representative sample of the Spanish population (N = 1,522) with popular science descriptions of de-extinction that varied only in metaphorical framing and assessed its downstream effects on misinformation, perceived scientific rigor, attitudes, funding preferences, and sharing intentions. Promotional metaphors increased misinformation relative to the neutral condition, while critical metaphors reduced it. These effects occurred without altering readers’ perception of the text’s scientific rigor—a dissociation we characterize as credibility without comprehension. Critical metaphors also reduced support for de-extinction, whereas promotional metaphors did not boost support above baseline levels in the neutral condition. Funding allocations and sharing intentions, though related to misinformation, were unaffected by metaphorical framing. We propose that metaphors function as epistemic conditioning devices: they influence downstream judgments not through direct persuasion, but by structuring the understanding upon which those judgments are formed. These findings bear on philosophical debates about the role of metaphors in science, the epistemic preconditions of democratic engagement with emerging technologies, and the limits of lexical precision as a remedy for public misinformation…(More)”.

Article by Stefaan Verhulst: “The policy and research fields often express concern over data, evidence, and funding gaps. Comparatively little attention is paid to the gap that arguably comes first: who gets to ask questions and set agendas that determine what (and what types of) data and evidence get produced.

Public policy, clinical trials, and other forms of research all exist downstream from choices about what is worth asking and investigating. The ability to define agendas and ask questions is a form of power. It is also inherently political—a reflection and perpetuation of existing power asymmetries. Questions decide what is measured, who is counted, which outcomes matter, and what is an acceptable answer. In fact, by the time a funder issues a call or a researcher designs a protocol, most of the consequential choices have already been made. To understand how agendas are set and why entire dimensions of knowledge and knowledge production are neglected, we need to talk about equity and inequity in questioning. We need to examine the distribution of power to pose, frame, prioritize, and resource questions, especially among people who are most affected by the answers.

This essay focuses on the different forms of power and exclusion that are central to the questions process. In subsequent pieces, we will examine the downstream effects of power on the evidence that gets produced, and explore possible responses…(More)”.