Stefaan Verhulst
Press Release: “As artificial intelligence increasingly relies on language and cultural data, Indigenous communities face unprecedented opportunities and significant risks. While Indigenous languages and cultural knowledge can help shape more inclusive digital futures, too often communities have limited influence over how their data is collected, governed, used, or shared.
To address this challenge, The GovLab, Microsoft, and UNESCO are launching the New Commons Incubator for Indigenous Languages and Culture, a new initiative designed to support Indigenous-led efforts to develop data commons that preserve, steward, and responsibly govern language and cultural resources in the AI era.
Data commons are shared governance frameworks that enable communities to collectively decide how their data is managed, accessed, and used while ensuring that benefits flow back to the communities themselves. By supporting the development of Indigenous-led data commons, the Incubator seeks to strengthen community agency, support language revitalization, and ensure that Indigenous peoples can participate in shaping the future of AI on their own terms.
The Incubator is a capacity-building initiative that provides mentorship, training, technical guidance, and proposal development support. Participants will receive an in-person opportunity to collaborate and network with other participants, followed by six months of workshops, one-on-one clinics, expert mentorships, and peer learning opportunities. The program is designed to help teams prepare stronger proposals and will end with a final showcase where participants present these to potential funders, partners, and collaborators…(More)”.
Book by David Hand: “Statistics and data science aim to extract understanding from data and guide decision-making. However, before applying any analytical tools, we need absolute clarity about what we want to know or accomplish. Ambiguous objectives inevitably lead to mistaken conclusions and flawed actions. This book investigates the deeper challenges of formulating clear questions and matching analytical methods to those questions – issues that apply as much to elementary statistical tools as to sophisticated techniques. Rather than focusing on standard statistical misuses or data provenance issues, this work examines the critical step of ensuring your analysis actually answers the question you mean to ask.
Drawing from collaborative work across finance, medicine, government, manufacturing, defence, and other fields, the book deliberately emphasises basic and familiar tools so the fundamental issues are accessible to everyone. Following John Tukey’s insight about the simplest problems of data analysis, the most detailed discussions centre on averages and comparisons between distributions, though the principles apply with even greater force to advanced methods that fewer people fully understand.
Key Features:
• Focusses on question formulation rather than computational techniques, addressing the step that precedes all successful data analysis
• Emphasises basic statistical tools (averages, comparisons) to make fundamental challenges visible to all practitioners
• Contains 130 text boxes presenting essential ideas in non-technical language, creating a “two-in-one” book accessible to both mathematical and non-mathematical readers
• Provides real-world examples drawn from diverse fields including finance, healthcare, government, manufacturing, and defence
• Offers a deep-dive analysis of a specific comparison method to illustrate the care required for precise statistical reasoning
• Presents a progression from general principles through detailed mathematical exploration to practical applications across various analytical scenarios
This book serves as an essential guide for statisticians, data scientists, researchers, and anyone who uses data to make decisions. Whether you’re a practitioner seeking to improve your analytical approach or a student learning to think critically about statistical questions, this work will help you use data analytical tools more effectively and avoid the costly mistakes that arise from asking the wrong questions of your data…(More)”.
Report by the University of Edinburgh: “… has recommendations for the Scottish and UK governments on steps that can be taken to ensure AI is developed and used in ways that create trust and deliver real benefits.
The report, Governing the Future: Recommendations from the Edinburgh Data and AI Exchange, brings together proposals on AI skills, national infrastructure, health data governance and democratic oversight…
A key recommendation for the UK Government is to establish a standing citizens’ assembly on AI and society.
A citizens’ assembly is a group made up of members of the public, selected through a process of random sampling designed to reflect the demographic makeup of the wider population, to explore societal issues and make policy recommendations.
The report argues this should be a permanent, properly resourced mechanism through which the public has a genuine and continuing role in shaping decisions about AI.
This mirrors the findings of a 2025 Ada Lovelace Institute survey, which found that 60 per cent of UK adults do not feel they have meaningful input on government decisions about AI.
Dr Morgan Currie, Senior Lecturer in Data and Society at the School of Social and Political Science, spoke at the event.
She said: “This report reflects what I heard at the Exchange and over and over again in my own research – that people want a say in the governance of technologies affecting them in their daily life, at their work, and increasingly in their interactions with government services. They want to reimagine technology for socially and environmental beneficial ends, beyond the narrow visions on offer by foreign-owned Big Tech.”..(More)”.
Article by Thomas Brent: “Latvia has introduced an element of citizen engagement to the evaluation of nationally funded research grants. The aim is to both create more connections between science and society, and to improve the quality of its evaluations.
The move comes as research funders across Europe are experimenting with ways to improve evaluation processes in the face of a sharpened focus on science’s impact on society.
Evaluators of grant applications submitted to Latvia’s Fundamental and Applied Research Programme (FLPP), the country’s main research funder, will this year have the option of consulting citizen feedback on challenges the public thinks science should focus on to inform their decisions.
“In recent years, both public discussions and policy-level debates in Latvia have highlighted the importance of demonstrating how publicly funded research contributes to society, the economy and the resolution of real-world challenges,” said a source at the Latvian Council of Science (LCS), which manages the FLPP.
“At the same time, research institutions themselves expressed interest in improving the project evaluation framework while continuing to ensure that funding is awarded to the highest-quality projects,” the source added.
The citizen input comes from a survey that was conducted between 25 February and 16 March 2025, to which 1,737 people responded. It gathered information on what the public views as problem areas for Latvia, and the role of science and technology in providing solutions to these problems.
A summary of these responses has been included as an annex to the FLPP 2026 call for proposals that evaluators can refer to, purely in an advisory manner, when judging proposals.
Results from the survey show that the main problem areas identified by the public were in healthcare and public health, followed by the development of new treatment methods and medicines, and then digital technology, data security and cyber security. At the bottom of the list was research aimed at acquiring new knowledge about the universe, matter and the laws of nature…(More)”.
Article by Jay Caspian Kang: “There will always be idealistic, ink-stained people who want to devote their lives to scholarly pursuits—their role to inspire young people to love ideas as they do. But this transfer, more than anything else in the academy, has been increasingly blocked by A.I. in the classroom. This past April, Jane Sloan Peters, a professor of religious studies, wrote a stirring Substack post in which she described a course she had designed, some years ago, about what people throughout history have been willing to endure for their faith. The class, called “Letters from Prison,” typically culminated in students trying to synthesize an overriding theme about what they had read. “When I began teaching this course four years ago, students struggled to come up with their own themes,” Peters wrote. But, through brainstorming and revision, the students would ultimately land on some understanding that both felt personal to them and proved they had grappled with the assigned texts.
Last year, the struggle ended—or, at least, got subverted. “Not one of my sixty students in ‘Letters from Prison’ struggled with this task,” she wrote. “I received tidy summaries of the text—the kind of compelling reviews you’d find on a book jacket—as well as perfectly vapid course themes that somehow took account of everything while not saying much.” What Peters suspected was that many of the students had asked A.I. to help. Like so many professors who have been confronted with the dispiriting new reality of student work, Peters adjusted, adding some handwritten brainstorming processes to her course, in the hope of making it A.I.-proof. But when she presented these new expectations to her students, something unexpected happened. “A wave of sadness washed over me, and I actually got choked up in front of the class.” Peters writes. “ ‘Before AI,’ I told them, ‘Students used to work hard to come up with their own ideas. I’d help, and they’d struggle, but they’d come to something that was their own. That doesn’t happen anymore and I grieve that.’ ”…(More)”
Article by Andrew Schroeder, Lauren Bateman, John Crowley, Satchit Balsari, Nishant Kishore, Jennifer Chan: “Meanwhile, global digital humanitarians and remote data providers have begun to heed the call to “fight Ebola with information”. Almost immediately following the epidemic declaration, Humanitarian OpenStreetMap Team (HOT) coordinated with Médecins Sans Frontières to launch a set of tasks for the global digital mapping community to use high resolution satellite imagery to update building footprints, roads, critical infrastructure, and other features for the creation of maps to aid in population estimations, community surveys, contact tracing, and other epidemic control efforts. Their efforts have been buttressed by releases of satellite imagery from Vantor. Satellogic and Planet have also made satellite imagery available.
Other key organizations have stepped forward as well. Flowminder has been regularly publishing analysis of human mobility flows to and from the epidemic affected areas based on digital device data from the mobile network operator Vodacom. These flow maps allow for rapid prioritization of epidemic control efforts based on anticipation of probable case transmission areas. WorldPop and GRID3 have published high resolution baseline population data as well as accurate health facility locations to aid with planning for health services and calculations of baselines for population exposures, among other essential analyses. The Armed Conflict Location and Event Data (ACLED) project is publishing regularly updated geospatially specific conflict data to assist in understanding changing security risks and other threats to affected communities and to the response effort. UN OCHA’s Humanitarian Data Exchange (HDX) platform is now hosting more than 40 datasets specifically relevant to the current response in DRC, although a reasonably high number of these are topically filtered extracts from OpenStreetMap.
If we look above the proverbial “water line” of the information iceberg at what is publicly visible, easily traceable, and known to be in use by response actors, we can see significant gains in key areas. These include small area demographics, building footprint and infrastructure mapping, remote sensing, event alerting, and case reporting.
Right at the waterline, where novel datasets and models are emerging now into routine visibility and usage, we find human mobility flows based on mobile device data. Flowminder’s data from Vodacom has been integrated into several risk models. At this level we also see a range of disease forecast models and dashboards, at different levels of spatial and temporal resolution, some of which may be based on very similar data and distributed across opaque and discontinuous channels.
What is most concerning though is what still lies below the waterline, where needs may be unmet and substantial gaps in data, information, and analysis likely exist. For instance, as of now there does not appear to be a common list of ebola treatment units available publicly. Data on safe burials is largely absent. Misinformation, as is now normally the case online, runs rampant without an obvious rumor cataloguing effort, or community information management. Logistics and supply chain needs are referenced constantly by response agencies, particularly for PPE and sanitation, but supply chain flows are largely undocumented publicly, and in any event not obviously connected to the epidemiological forecasts and risk assessments despite clear calls from WHO for strategic prepositioning of essential supplies. Health facility locations are widely circulated, but facility-level capacity in terms of staffing, equipment, and supplies, is ambiguous at best. Epidemiological forecasts and risk analyses are not obviously connected to any particular workflows on logistics and supply chain…(More)”.
OECD Report: “People expect their government to act quickly, adapt to change and respond effectively, putting pressure on public institutions to keep up. Digital technologies and data are integral to meeting these demands: they have become core infrastructure for governments to perform and address today’s policy and service delivery challenges.
This reportpresents results from the OECD Digital Government Index (DGI) and the Open, Useful and Re-usable Data Index (OURdata), illustrating how governments across 36 OECD countries and 8 accession countries have been improving coherent, effective and human-centred digital transformation in government in recent years...(More)”.
Article by Geoff Mulgan: “We live in a world full of lies, distortions and misinformation. Should we have rights to be told the truth, or at least not to be lied to? If a government issues a statistic, a report, or a warning to its citizens, should any rights guarantee that it’s based on the best available information? Should there be penalties if a company or a political party, knowingly lies? If a doctor gives you a diagnosis, should it be your right that the diagnosis is based on the best possible medical knowledge? If a company has your pension invested in it, should any rights guarantee that their accounts are as accurate as possible?
It might seem reasonable that any institution which claims to serve us should give us the respect of telling the truth. Yet no constitution guarantees that right. The US constitution protects rights of free speech, but no rights to truth. British law supports many rights, but not this one. Nor does the European Convention on Human Rights, which supports freedom of thought and expression, but no rights to information or truth.
In this piece I set out why such rights are needed, and what they might mean. I show why existing laws in finance and consumer advertising can be built on, as well as recent ones designed to address the problems of deception on social media. I show why some of the assumptions of liberalism have become a barrier to action, and why freedom depends on truth. And I show why rights to truth could help harness the populist anger against the deceit and self-serving of powerful institutions in a more constructive direction. Finally, I address the main counter-arguments, which essentially say that no-one, and especially governments, can be trusted to determine truths of any kind…(More)”.
Report by the US Government Accountability Office: “Agencies can use more than 100 federal data sources—or a combination of them—to verify if recipients meet the eligibility criteria for federal programs throughout the award life cycle (which includes pre-award screening, post-award monitoring, and payment validation). As of September 2025, these included 28 data sources in the Do Not Pay working system (DNP) or designated for inclusion in DNP. However, weaknesses in data interoperability may hinder agencies’ ability to efficiently determine award and payment eligibility.
Data interoperability is the ability to share and disseminate standardized data in a way that is efficient, consistent, and accessible across different systems and users, for which high-quality data are essential. Without it, the risk of improper awards or payments increases, and the potential use of artificial intelligence and advanced analytics to assist agencies in making eligibility determinations is limited.
GAO found that, for more than 30 years, several laws and guidance have established general requirements related to data interoperability but have not established specific requirements for enforcing interoperability, such as for recipient eligibility data, throughout the federal government. Many of the data sources GAO identified, including those in DNP, were created to comply with legal requirements or to manage specific federal programs—not to support eligibility determinations for other agencies.
GAO also found a variety of obstacles and challenges that can affect the interoperability of the nine selected data sources that agencies may use for eligibility determinations (see figure).
Summary Comparison of Key Elements GAO Assessed to Eligibility Data Interoperability Needs and Observations
GAO also found that insufficient or improperly documented validation rules contributed to data quality issues. All nine selected data sources had data quality issues (e.g., missing, invalid, and duplicate data), and seven data sources had inconsistences between them, such as overlap in mutually exclusive data. These data quality issues undermine data reliability and interoperability for agencies seeking to make eligibility determinations…(More)”.
Editorial by Qi R. Wang: “Understanding how people move through cities is fundamental to epidemic preparedness, transportation planning, and climate policy. Yet for most of the world’s cities, particularly across the Global South, reliable mobility data simply does not exist. The traditional approach of conducting household travel surveys is expensive and slow; passively collected data from mobile phones, while transformative in data-rich countries, remains scarce where digital infrastructure is limited. This asymmetry creates a troubling paradox: the cities most in need of mobility-informed planning are precisely those with the least data to support it. For nearly eight decades, the gravity model has served as the default tool for filling this gap. The model estimates travel flows between locations as proportional to their populations and inversely proportional to the distance separating them. Its elegance lies in its simplicity: only population and distance are required. But that simplicity comes at a cost. The gravity model captures broad flow distributions while struggling to accurately estimate travel between specific pairs of neighborhoods. In recent years, deep learning approaches such as Deep Gravity have improved predictive accuracy by incorporating richer features of the built environment, but they introduce a new problem: overfitting to observed patterns and an inability to generalize to cities where no mobility observations exist. The field has been caught between interpretability and accuracy, between transferability and expressiveness (Fig. 1). In this issue of Nature Computational Science, Jinming Yang and colleagues introduce neuroGravity, a physics-informed deep learning framework that bridges this divide…(More)”.