Stefaan Verhulst
Index by Reporters Without Borders (RSF): “For the first time in the history of the Reporters Without Borders (RSF) World Press Freedom Index, over half of the world’s countries now fall into the “difficult” or “very serious” categories for press freedom. In 25 years, the average score of all 180 countries and territories surveyed in the Index has never been so low. Since 2001, the expansion of increasingly restrictive legal arsenals — particularly those linked to national security policies — has been steadily eroding the right to information, even in democratic countries. The Index’s legal indicator has declined the most over the past year, a clear sign that journalism is increasingly criminalised worldwide. In the Americas, the situation has evolved significantly, with the United States dropping seven places and several Latin American countries sliding deeper into a spiral of violence and repression…(More)”.
Article by Manije Kelkar: “A CEO of a mid-sized nonprofit recently shared her frustration: after nearly two years of trying to hire for data roles, her organisation had little to show for it. Candidates were either unaffordable, inexperienced, or simply unavailable.
It’s an increasingly familiar story…
Before asking how to solve the talent gap, it’s worth unpacking what we mean by “data talent”. Data talent is not a single role—it is a spectrum of capabilities required to design, manage, and use data effectively.
At one end are Monitoring, Evaluation, and Learning (MEL) professionals who define what data should be collected and how it aligns with programme design and decision-making. Then there are analysts who interpret this data—building dashboards, generating insights, and supporting reviews. Behind the scenes are those who build and maintain data systems: MIS platforms, databases, and pipelines. At more advanced stages, organisations may engage data scientists to explore deeper patterns and predictive insights.
Expecting a single hire to cover this entire spectrum is unrealistic. Yet many organisations implicitly do exactly this—hiring “a data person” and hoping it will solve all their data-related challenges. Across nonprofits at different stages of data maturity, a clear pattern emerges: the challenge is not just a shortage of talent, but how narrowly the problem is defined. It is often viewed as a hiring gap rather than an organisational one.
Even where organisations are able to hire, the impact of that hire is often limited in the absence of complementary investments. Becoming data-driven is less about a single role and more about building an enabling environment.
Here are five shifts organisations can make alongside hiring…(More)”.
Book by Cornelia C. Walther: “In an age where algorithms shape our every move, this book offers an inspiring reframe: What if AI could amplify what makes us both human—and humane?
Artificial Intelligence for Inspired Action explores how natural intelligence can guide ProSocial AI. Drawing on the POZE paradigm, Cornelia C. Walther weaves global stories and systemic insights to spotlight hybrid intelligence—where human values and machine power meet. As reliance on AI risks slipping into dependence, she proposes double literacy—human and algorithmic—to reclaim agency. A wake-up call and guide in one, this book invites changemakers to lead with integrity and design a future worth living…(More)”.
Paper by Fatih Kansoyn and Yuhao Huo: “Artificial intelligence makes data more productive, but it also makes data more costly to govern. This paper asks where that governance cost shows up in firms’ own risk disclosures. Using roughly 84000 firm-years of SEC annual filings for US listed firms from 1994 to 2023, the paper builds layered text and LLM measures to separate AI invention from AI adoption and relates both to disclosed attention to data breach risk. AI adoption is associated with roughly 5 per cent higher breach-risk attention relative to the sample mean; AI invention is economically negligible once both margins enter the same specification. The wedge survives an explicit non-AI digitisation placebo built from the same filings. Among adopters, breach-risk attention is highest where deployment is customer-facing. Firms that explicitly connect AI to breach vulnerability describe it as expanding exposure in 101 of 103 directional statements. Supplementary evidence from staggered state Data Breach Notification laws is directionally consistent with the disclosure results…(More)”.
Article at The Economist: “The life of American government beancounters is tough, and not just at cocktail parties. They have a hard time persuading people to talk to them at work, too. A decade ago nearly nine in ten Americans, when approached, agreed to fill out the Current Population Survey, which is administered to about 60,000 households each month and asks about, among other things, employment. Fewer than seven in ten do so now (see chart 1). For the Consumer Expenditure Survey, which tries to capture 3,700 households monthly, the response rate is down from 68% to 40%…(More)”
Article by Johan Harvard, Kurt McLauchlan, David Milestone, Barbara Ubaldi: “Ministers know they are running out of time and money. A fly on the wall in every minister of health’s office will tell you the same story: an inbox full of complaints, stories about backlogs, budget warnings from the finance ministry and messages from the prime minister’s office asking for “quick wins”.
The problem is that there are very few quick wins in health today. Health systems are understaffed, demand is rising and budgets are failing to keep pace. More than 4 billion people around the world still lack access to essential services. Health systems are expected to do more with less – and to do it now.
AI in Health: Promise and Pressure
Everyone is saying that artificial intelligence could be the answer, but no one knows where to start and there is significant risk in getting it wrong. What ministers need isn’t another one-off sales pitch, but a way to cut through the noise and to identify where AI can actually help. To do that they must work out what to prioritise politically and how to turn potential into results.
This paper introduces a practical framework that can help governments decide where AI is most usefully applied and outlines the enablers required to implement it at scale…(More)”.
Essay by Nick Carr: “…The construction of telecommunication networks required enormous capital and extensive managerial coordination. In the United States, media became big business, as the rise of Western Union signified. To inventors, entrepreneurs, and corporate executives, the public’s celebration of communication proved a boon. Not only did it reinforce their messianic sense of self-importance; it served their business interests. It guaranteed them eager customers, enthusiastic investors, and indulgent regulators. As the pace of technological progress quickened, each advance in media systems triggered a new burst of millenarian rhetoric. Nikola Tesla, in an 1898 interview about his plan to create a wireless telegraph, said that he would be “remembered as the inventor who succeeded in abolishing war.” Not to be outdone, his rival, Guglielmo Marconi, declared in 1912 that his invention of radio would “make war impossible.”
Such cheery predictions were put to an early test in the summer of 1914. In the immediate aftermath of the June 28 assassination of Austrian Archduke Franz Ferdinand by a Serbian nationalist in Sarajevo, hundreds of urgent diplomatic messages raced between European capitals through recently strung telegraph and telephone wires. As the historian Stephen Kern has described, the rapid-fire dispatches quickly devolved into ultimatums and threats. Rather than calming the crisis, they inflamed it. “Communication technology imparted a breakneck speed to the usually slow pace of traditional diplomacy and seemed to obviate personal diplomacy,” Kern writes. “Diplomats could not cope with the volume and speed of electronic communication.” Diplomacy, a communicative art, had been overwhelmed by communication. By August, World War I was under way…(More)”.
Article by Stefaan G. Verhulst and Roshni Singh: “Artificial intelligence systems are increasingly designed to remember us. Whether answering a question, drafting an email, or recommending a course of action, modern AI systems draw on accumulated knowledge about a user’s preferences, behaviors, goals, and past interactions to function effectively. This capacity for context — persistent memory about who we are and what we do — is not a secondary feature. It is foundational to how these systems generate value.
But context in AI is more than a technical convenience or feature. It is also a source of risk. The accumulation and reuse of personal information introduces privacy vulnerabilities, particularly when data from different domains is aggregated into a single, unified memory. This is, of course, not a new concern: as Helen Nissenbaum argued in Privacy in Context, privacy depends on maintaining appropriate information flows within specific social contexts, and risks emerge when those boundaries are collapsed. What AI changes is the scale, speed, and inferential power of such aggregation, turning what were once discrete data linkages into continuous, dynamic systems capable of generating new insights, predictions, and vulnerabilities far beyond the original contexts in which the data was produced.
And the persistence of context raises deeper questions about cognitive dependence: when AI systems continuously shape the informational environment in which users think, they do not merely respond to us but influence how we understand ourselves and make decisions. In doing so, they risk constraining what we have described as digital self-determination: the ability of individuals and communities to meaningfully shape the conditions under which their data is (re) used and how it, in turn, shapes them — shifting agency from the user to the system in often opaque and difficult-to-contest ways.
These risks are not limited to one category of AI. They apply across AI systems that store and reuse user data — from large language models and recommendation engines to agentic systems that act autonomously on a user’s behalf. What this article examines is not a particular technology, but a structural feature common to many: the use of context as memory, and the tradeoffs that follow.
Context is often treated as the accumulation of user data, but this framing is incomplete. Context is better understood as the relational structure that gives information meaning by situating it within social, temporal, and functional relationships. It is not simply what is stored, but how information is organized, linked, and interpreted within a given frame. Without these relationships, data may remain present but lose meaning or be misapplied across situations. As Jessica Talisman further elaborates, this spectrum runs from statistical proximity to formal logical commitment; AI systems that conflate these distinct levels of relational strength risk treating correlation as meaning.
In what follows, we draw on emerging writing on AI memory, context, and human-AI interaction to explore three interconnected dimensions of this problem. First, we examine why context matters so much for AI performance, and why it is better understood as a relational structure than as simple data storage. Second, we analyze the privacy risks that arise when contextual boundaries collapse. Third, we consider the cognitive risks of persistent memory: the possibility that AI systems come to shape not only what users do, but how they think. Across these dimensions, we also consider the implications for digital self-determination — that is, the extent to which individuals and communities retain meaningful agency over how they are represented, interpreted, and acted upon within context-aware AI systems. These concerns are especially acute for children and young users, for whom both data exposure and cognitive development are at stake…(More)”.
Article by Stefaan Verhulst: “Last week, at Jesus College, Cambridge University, the inaugural cohort of Digital Statecraft Fellows gathered — alongside a diverse group of policymakers, technologists, scholars, and practitioners — to grapple with a deceptively simple yet profound question: how do we govern in the age of AI?
The convening offered a highly needed space where theory met practice; where geopolitical realities, technical architectures, and governance responses were debated not as abstractions, but as institutional design challenges.
The discussions, grounded in the principles of the Digital Statecraft Manifesto, revealed a field at an inflection point. Digital statecraft is no longer just about digitizing services or regulating platforms at the margins. It is about rethinking the state itself as a coordinator in a world where AI systems, data infrastructures, and global platforms increasingly mediate social, economic, and political life.
Below are my ten high-level takeaways from the convening — signals, perhaps, from the frontier of digital statecraft. In keeping with the spirit of the convening — held under Chatham House Rules — I will not attribute specific remarks to individuals, but instead reflect some of the collective insights that emerged across the discussions…(More)”.
Paper by Mattia Mazzoli et al: “The COVID-19 pandemic served as an important test case of complementing traditional public health data with nontraditional data, such as mobility traces, social media activity, and wearable data, to inform real-time decision-making. Drawing on an expert workshop and a targeted survey of epidemic modelers in Europe, this study assesses the promise and the persistent limitations of such data in pandemic preparedness and response. We distinguish between “first-mile” challenges (obstacles to accessing and harmonizing data) and “last-mile” challenges (difficulties in translating insights into actionable policy interventions). The expert workshop, convened in March 2024 in Brussels, brought together 50 participants, including public health professionals, data scientists, policymakers, and industry leaders, to reflect on lessons learned and define strategies for better integration of nontraditional data into epidemic modeling and policymaking. The accompanying survey, gathering experiences from 29 modelers, offers empirical evidence of the barriers faced by modelers during the COVID-19 pandemic and highlights areas where key data were unavailable or underused. The experiences collected through the survey and workshop resulted in ten key actions and three overarching recommendations for public entities, data providers, and stakeholders. Our findings reveal ongoing issues with data access, quality, and interoperability, as well as institutional and cognitive barriers to evidence-based decision-making. Approximately 66% of all datasets had at least one access problem, with data sharing reluctance for nontraditional sources being double that of traditional data (30% vs 15%). Only 10% of respondents reported that they could use all the data they needed. These limitations included issues related to timeliness and granularity of data, as well as issues with linkage, comparability, and biases. To overcome these hurdles, we propose a set of enabling mechanisms, including data inventories, standardization protocols, simulation exercises, data stewardship roles, and data collaboratives. For first-mile challenges, solutions focus on technical and legal frameworks for data access. For last-mile challenges, we recommend fusion centers, decision accelerator laboratories, and networks of scientific ambassadors to bridge the gap between analysis and action. We argue that realizing the full value of nontraditional data requires a sustained investment in institutional readiness, cross-sectoral collaboration, and a shift toward a culture of data solidarity. Grounded in the lessons of the COVID-19 pandemic, the study can be used to design a roadmap for using nontraditional data to confront a broader array of public health emergencies, from climate shocks to humanitarian crises…(More)”.