Stefaan Verhulst
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)”.
Article by Taylor Butler: “A sales receipt, a web search, a satellite image, a job posting that expired two years ago; the seemingly mundane multitudes of data being created by daily activities hold unique and valuable insights for researchers to parse.
What is alternative data, and what makes it so useful for research? We’re not talking about fringe science here, but the creative application of non-traditional sources of data to answering research questions. I spend a lot of time working with a catalog of datasets that captures a vast and varied array of human behavior and world phenomena, and I am constantly inspired by the innovative problem solving researchers employ to extract answers from new sources and diverse perspectives.
What makes data “alternative”?
Alternative data, with its many alternate names (organic [data], novel, found, digital trace, unstructured, observational, derived, etc.), is essentially any data that was generated for some purpose other than research – typically as a byproduct of some administrative, commercial, or technical process – and then utilized for research. Common sources of “alt data” include satellite imagery, mobile-phone records, usage data, administrative records, transaction records, job postings, social media and internet activity, sensor data, and much more.
This differs from traditional datasets that are designed, built, generated or measured, such as structured surveys, experimental data collection, or official government reporting. Alt data offers novel “proxy” measurements of real-time, real-life behavior and phenomena, giving researchers unique perspectives to new and old questions. While economics has paved the way in alt data usage, adopting large-scale administrative and private sector data in the study of market trends (Einav & Levin, 2014), many fields are also implementing new data streams into their research, from global health to climate change, public policy and more.
Take an age old question like: does spending time in nature improve mental health? There are many ways researchers go about answering this, the most traditional method typically being a survey or maybe even a random assignment experiment. But these researchers (Chandra Mouli et al., 2026) combined a traditional data source (CDC health measures) with real-life measures of behavior (cell phone mobility data) and neighborhood green space (NASA satellite imagery). Looking at nine US metro areas, they found that neighborhoods where residents actually visited nearby green space had lower rates of poor mental health; the presence of parks alone did not explain it. No single one of these three sources could have provided this nuanced insight; the combination of disparate data sources provides researchers with opportunities to explore old questions in creative new ways…(More)”.
Article by Nitasha Tiku: “At a beachfront resort in San Juan, Puerto Rico, SpaceX founder Elon Musk joined a three-hour discussion on humanity’s dire fate if artificial intelligence started to rapidly improve.
The afternoon panel was part of a private conference hosted in early 2015 by the Future of Life Institute, a new nonprofit whose founders — mostly outsiders to AI — believed the nascent technology would grow so powerful it could render humans extinct.
The first presenter was Oxford University philosopher Nick Bostrom, author of the recent bestseller “Superintelligence: Paths, Dangers, Strategies,” whose slides argued that AI could either help humanity spread across the cosmos or drive it to extinction.
The 80-person guest list was filled with bold-faced names, including the co-founders of DeepMind, acquired by Google the year before, and three future co-founders of OpenAI including Musk, who backed the nonprofit research lab’s launch later that year.
The conference aimed to legitimize concerns about AI’s risks within the industry and spur research into preventing them, physicist and Future of Life Institute co-founder Max Tegmark wrote in his book “Life 3.0: Being Human in the Age of Artificial Intelligence.” The elite gathering made it “harder to claim that people concerned about AI safety didn’t know what they were talking about,” he wrote.
Predictions involving human extinction, built on themes aired at the 2015 meeting, reached a wider audience in recent weeks after they were invoked by employees at leading AI firms who captured the world’s attention.
On Tuesday, the Future of Life Institute hosted a day-long event in Washington called the Pro-Human Assembly where Sen. Bernie Sanders (I-Vermont) and former Trump adviser Stephen K. Bannon in successive speeches called AI a threat to the species. “I had to metaphorically pinch my arm to make sure I wasn’t dreaming,” Tegmark told The Washington Post. “People are freaking out about it all across the political spectrum.”
Thought experiments once deployed to sway AI insiders are now shaping the public imagination, circulating through Washington and influencing how lawmakers plan to govern a multitrillion-dollar industry. But some AI and policy experts wonder if the parables could lead decision-makers astray…(More)”.
Report by the Digital Impact Alliance: “Governments, researchers, and communities are increasingly dependent on climate data for informed decision-making — from early-warning systems and municipal planning to agricultural insurance payouts. Yet keeping the platforms behind that data running is a challenge that gets far less attention than the data itself, as their infrastructure is easier to build than it is to maintain. DIAL’s prior research — Beyond the tipping point: How climate data can decide our future and Bridging the climate data deficit: Lessons from the frontlines — examined the governance models and use cases that make climate data valuable once it is available. This report turns to the question that sits upstream of both: what allows that data to remain available and usable over time…(More)”.
Paper by Steve MacFeely and Ashley Ward: “Official statistics have long provided a foundation for evidence-informed policy-making, offering professionally independent, quality-assured and transparent evidence to support democratic decision-making. Yet the contemporary policy environment presents statistical systems with a difficult set of pressures. Demand is growing for faster, more granular and more actionable evidence, particularly during crises, while policymakers increasingly draw on dashboards, models, artificial intelligence and non-traditional data sources. These tools can improve timeliness and analytical capacity, but they also raise risks relating to bias, opacity, representativity, uncertainty and accountability. This paper examines the evolving role of official statistics in this context, arguing that their value lies not in automating or determining policy choices, but in anchoring them. It distinguishes between evidence-informed and data-driven decision-making, warning that the latter may imply a technocratic shift in which data appear to substitute for human judgement. Drawing on Irish and international examples, the paper considers the institutional, methodological and democratic challenges facing official statistics, including data access, public trust, communication, artificial intelligence and the post-truth information environment. It concludes that official statistics remain essential democratic infrastructure: they should inform decisions, clarify trade-offs and support accountability, while remaining subordinate to context, deliberation and judgement…(More)”.
Report by UNDP: “Every Language Matters (ELM) is a global-to-local initiative that turns hard-won country lessons into coordinated public action, building on two years of work through the UNDP Local Language Accelerator. To illustrate how value and trust are created in AI innovation, the initiative introduces the Data-to-AI Value Chain (UNDP’s framework for overcoming barriers to inclusive language AI between data and deployment). Governments, researchers, funders, technology partners, and communities each hold part of the solution. Every Language Matters drives action towards a sustainable ecosystem of trust and innovation at global scale – one that promotes discoverability of promising work, mobilizes investment, and inspires new partnerships. The Data-to-AI Value Chain brief is a collaboration between Current AI and UNDP. Diffusion alone is not a development outcome; it is a signal that certain enabling conditions are already in place. Importantly, it is not evidence that those conditions are serving the countries and communities where AI is being diffused. The Data-to-AI Value Chain for linguistic and cultural diversity is a core feature of AI agency, and a delivery mechanism for the Every Language Matters Initiative at UNDP…(More)”.
Paper by Anastasija Nikiforova et al “Data sharing is increasingly essential for digital government and data-driven innovation, yet many public organizations remain reluctant to make their data openly available. While prior research has examined factors influencing open data adoption, little theoretical work explores why resistance persists within public agencies. This study develops an Innovation Resistance Theory (IRT) model tailored to government data sharing to identify predictors of organizational resistance. An initial model was derived from literature and refined through interviews with 21 public organizations across six European countries. The resulting model – IRT4DS – identifies 39 barriers spanning usage, value, risk, tradition, and image dimensions, and 23 countermeasures mapped to the most critical barriers and the actors responsible for addressing them. By extending IRT into the context of governmental data sharing, the study advances theoretical understanding of why public data often remains closed and provides actionable guidance for policymakers seeking to design enabling data ecosystems and reduce structural and cultural barriers to OGD adoption…(More)”.
Toolkit by the Office of National Statistics (UK): “When you are designing and implementing a new policy, programme, or process, it can be hard to see how the planned activities will lead to the desired outputs. There are often complexities which can affect the success of the initiative, such as assumptions we have to make about the effects of our actions, and dependencies on other stakeholders. Only by clearly considering and highlighting these complexities can you maximise the chances of achieving your aims and effectively evaluating the outcomes. Theory of Change (TOC) is a methodology for planning, monitoring, and evaluating an initiative. It enables the responsible team to clearly consider and highlight these complexities, and to agree these with relevant stakeholders. TOC is a tool that strengthens decision-making processes by ensuring all stakeholders are aligned and that you have identified existing evidence, assumptions, and associated risks…(More)”.