Stefaan Verhulst

Toolkit by the Canadian Institute for Health Information: “…houses an evolving collection of resources, documents and tools designed to support implementation of the Health Data Stewardship Framework. Complementing these tools is the Pan-Canadian Resource Inventory (XLSX), which brings together a curated collection of Canadian and international data stewardship resources that support pan-Canadian health data access, sharing and use.

Who is this implementation toolkit for?

This toolkit is designed for organizations, teams and individuals seeking to understand, assess and strengthen health data stewardship practices. It is for anyone involved in the capture, management, use and governance of health data.

You can use the resources within this toolkit to

• Support operationalization of the framework to improve data sharing, access and use
• Enable consistent monitoring of progress over time
• Learn from leading and emerging stewardship-related practices across health systems

Content will continue to expand as new tools are developed and as best practices evolve to continuously support you in your health data stewardship journey…(More)”

World Development Report 2026 by the World Bank: “In a time when development progress has dipped to its weakest pace in 75 years, artificial intelligence (AI) offers developing economies a rare path to greater prosperity. World Development Report 2026 provides the first comprehensive assessment of what AI means for these economies—how firms and governments are already using it, what is holding them back, where the largest gains may lie, and how the risks can be managed.

Its central message is both practical and ambitious. Developing countries do not need to build trillion-dollar, all-purpose models to benefit from AI. But importing AI tools is also not enough. Countries must adapt AI to local languages, institutions, data, and development needs; ensure that national systems can work seamlessly with multiple platforms and providers; and steadily build the skills and infrastructure needed to do more. Low-cost tools—“small AI”—can put scarce expertise within reach of millions, through text messages, voice calls, basic phones, and other technologies that work even where electricity, computing power, and internet access are limited. The gains are already visible. AI is helping to accelerate medical screening, assisting farmers with more accurate weather forecasts, and aiding teachers in creating better lessons for students.

The Report offers a clear framework—adopt, adapt, and advance—for the road ahead to help countries capture AI’s benefits without wasting scarce resources or deepening dependence on any single supplier. Earlier technological revolutions left much of the developing world behind. This landmark edition of the World Development Report offers developing economies a roadmap to prevent a repeat of that outcome…(More)”.

Guidebook by Jane Anderson and Vanessa Smith: “…outlines data types, legal and governance foundations, provenance, collection & consent, storage infrastructure & access, use & reuse, attribution & accountability, and common misconceptions…(More)”.

Article by Merlyn Thomas and Paul Brown: “Google has rolled back its new AI tool that allowed people to create fake images on top of its satellite imagery, following a BBC Verify report in which experts raised concerns about misuse and the potential to spread misinformation.

Less than 48 hours after the tech giant announced the integration of its AI image generator Nano Banana 2 into Google Earth, it said it was pausing the feature “while we work on implementing stronger guardrails”.

In its statement on Friday, Google said it had seen “people sharing screenshots of generated imagery that appear to violate our policies”.

The retracted feature enabled users to use text prompts to generate false scenarios on top of a base layer of real satellite, aerial or 3D imagery – which could then be downloaded and shared on other platforms.

“We know that people uniquely trust Google Earth for a reliable view of the world,” it added.

Experts told BBC Verify the tool could be used to spread misinformation with the appearance of Google’s legitimacy – and highlighted the potential for bad actors to abuse it.

A collapsed Eiffel Tower, a sinkhole swallowing the Great Pyramid of Giza and Russian tanks in Ukraine’s capital were among the images BBC Verify was able to create when testing the feature, which initially launched on Thursday…(More)”.

Book edited by Mark Findlay, and Noha Lea Halim: “In this volume, young voices craft a fresh exploration of living and working in virtual worlds. Using Alice in Wonderland as a guiding metaphor, contributors address profound and urgent questions: Who am I online? Who decides what is possible in virtual worlds? How can these spaces be made safe, fair, and empowering?

Through specially commissioned short essays and reflections, emerging voices with extensive experience of digital environments evaluate key challenges of governance and personhood within the metaverse. Drawing on diverse disciplinary insights, chapters investigate how design choices subtly function as constraints and possibilities, how identities transform across platforms and how communities build their own norms. Ultimately, the book emphasises the importance of treating the metaverse as a lived social space and prioritising the voices of its first-hand navigators. It outlines strategies for designing policies that protect individuals while simultaneously promoting agency, imagination and inclusion within virtual worlds like the metaverse, both now and in the future.

Governing Virtual Wonderland is an essential read for scholars, students, and those interested in regulation and governance in the new digital age. It is also useful for those across the disciplines of technology and AI, law, political science, philosophy of data science and computer science…(More)”.

Map by Power for Democracies: “AI has been reshaping how information spreads, how governments operate, and how citizens participate. Deciding where to direct limited resources to protect democracy from these rapid developments has been far from straightforward.

While various research communities have been working in parallel, producing valuable but fragmented analyses, no single resource exists that brings these findings together into a coherent body of work….

Given how fast AI is developing, we began filling these gaps as a priority. Over six weeks, our team reviewed a wide body of existing literature and selected ten leading frameworks mapping AI threats.

The selected literature spans multiple levels of abstraction, disciplinary lenses, and democratic contexts – from the Carnegie Endowment for International Peace’s broad survey of AI and democracy to the Brennan Center for Justice’s focus on US electoral integrity, to the global perspective of the 2026 International AI Safety Report.

We extracted and standardised entries from these sources into a unified, publicly available database containing:

• 144 documented AI threats to democracy
• 103 pro-democracy strategies for addressing them
• 44 independent opportunities for strengthening democratic resilience

Each entry is mapped onto the aspects of democracy it affects, using an adapted International IDEA framework of democracy that clusters around: citizenship, law and rights; representative and accountable government; civil society and popular participation; and a category covering international dynamics…(More)”.

Initiative by Patrick Grady: “For seventy years, prominent researchers, executives, and public intellectuals have offered specific dates for the arrival of artificial general intelligence, superintelligence, the technological singularity, and the collapse of large parts of the labour market. This archive collects those forecasts: both those the calendar has already refuted, and those still open but on the record.

The purpose is neither ridicule nor vindication. Public forecasts shape research funding, policy, and public expectation, and have a non-negligible impact on individual and societal welfare. It’s important to view present predictions within this context…(More)”.

Paper by John A. List, Matthias Rodemeier, Sutanuka Roy & Gregory K. Sun: “Behavioral interventions have become central to modern public policy, but their empirical promise remains contested because estimated treatment effects often appear small. We argue that a policy response is economically meaningful only relative to the response generated by alternative policies. We assemble more than 1,200 estimates from over 600 studies comparing “nudges” and traditional price interventions in the markets for cigarettes, alcohol, influenza vaccination, electricity, and residential water. Translating nudge effects into equivalent price changes, we find that behavioral interventions often correspond to enormous fiscal interventions, from an 11% tax on electricity to a 100% subsidy on influenza vaccinations. Nudges are also more cost-effective than price instruments in all markets, but cost-effectiveness does not predict the welfare ranking of policies. Using a behavioral extension of the Marginal Value of Public Funds, we show that nudges have high welfare returns at the margin, while price instruments often generate larger total surplus at scale…(More)”.

Book by Evangelos Pournaras, Srijoni Majumdar, Carina Hausladen, Dirk Helbing: “Interfacing Artificial Intelligence (AI) with democracy is one of the most profound challenges of our times. On the one hand, AI comes with opportunities to overcome long-standing challenges in democracy, such as low participation in deliberative and voting processes with poor representation of people. On the other hand, new risks arise from AI algorithms that are privacy-intrusive, biased, manipulative, spread misinformation and influence election results. Moving beyond the over-simplistic question of whether AI is good or bad for democracy, the Handbook on Democracy in the Era of Artificial Intelligence asks instead: how to upgrade democracies and the principles they are built on, using AI? How to engage with AI and on what terms? Which new values and design principles are required to build democratic resilience? In 34 chapters by 59 authors across the world from different disciplines, we explore how AI can empower collective intelligence for democracy (Part 1) and what is the future of deliberative democracy using large language models and social media (Part 2). We also illustrate the role of AI for building resilient self-governance systems (Part 3) and the challenges of transforming democracy in the age of AI (Part 4). We conclude with broader perspectives (Part 5) that re-imagine the interplay of democracy and AI…(More)”.

Book review by Luciano Magaldi Sardella of “The Credibility Crisis in Science by Thomas Plümper and Eric Neumayer: “…The scholarly landscape into which this book enters has been shaped by growing recognition of publication bias – the documented phenomenon that studies reporting positive or statistically significant findings are substantially more likely to be published than those reporting null results or failures to replicate. Research by Franco, Malhotra, and Simonovits found that among social science experiments that were peer-reviewed and approved before data collection, 91 per cent of published studies reported positive results, while only 72 per cent of the total conducted experiments did so. This systematic skewing of the published record, combined with growing awareness of questionable research practices (QRPs), has catalysed calls for reform across multiple disciplines. Plümper and Neumayer’s work addresses this ferment by providing a comprehensive taxonomy of how empirical findings become corrupted.

The authors’ central conceptual innovation lies in their tripartite taxonomy of empirical manipulation. They distinguish carefully between three categories often conflated in public discourse. First, outright data fabrication – the wholesale invention of findings – exemplified by the case of Diederik Stapel, the Dutch social psychologist who, between 2000 and 2011, fabricated data for at least 55 publications in leading journals including Science. Second, data manipulation – the alteration of raw data to produce desired outcomes. Third, and most significantly for their argument, “tweaking” – the intentional selection of research designs, model specifications, and analytical protocols based on the results they yield, often without explicit disclosure of these choices…(More)”.