Stefaan Verhulst
Press Release: “Today, during the Committee on World Food Security, leaders from governments, UN agencies, technology companies, NGOs, philanthropies, and financial institutions announced the Intentional Data Community for Food and Nutrition Security (IDC), a new multi-stakeholder initiative to strengthen and connect the data systems used to anticipate, prevent, and respond to food and nutrition crises. The IDC will work to protect and strengthen critical data systems, increase investment in locally owned data collection and analysis, and better connect data to decisions and financing so governments and communities can act before crises hit.
The effort comes as funding for food and nutrition security data systems is declining even as conflict, climate, and economic shocks increase pressure on vulnerable communities. A powerful El Niño and disruptions in the Strait of Hormuz are adding to risks facing food, fuel, and fertilizer markets — underscoring the need for reliable early warning and risk monitoring and the ability to act on them.
New analysis undertaken by IFPRI as part of the VITAL Data initiative shows that funding to priority data systems was cut by an average of 31% in 2025. This includes cuts to FEWS NET, the Integrated Food Security Phase Classification (IPC), and, crucially, the household surveys on which they rely. During the launch event, leaders highlighted the consequences: a reduced ability to reliably understand rising risks; track the severity of malnutrition, famine and hunger; target food, nutrition, and other needed assistance; and help farmers, communities and governments take anticipatory action ahead of shocks, including floods and drought.
“The world has extraordinary tools to anticipate food crises, but those tools only work if we invest in the data and put it in the hands of people who can act,” said Simon Winter, Vice President of the Reimagining Humanitarian and Nutrition Security team at The Rockefeller Foundation’s public charity, RF Catalytic Capital. “As funding declines and El Niño threatens communities around the world, we need to protect and improve needed early warning and data systems and help local and national leaders use better information to act earlier, save lives, and protect livelihoods.”
“The cost of good data is small compared to the cost of responding too late. By strengthening food security information systems, the IDC can help governments and partners act earlier, target assistance better, and protect more lives. As the leading source of information on acute food insecurity, we know that timely and accurate data can be lifesaving for vulnerable communities everywhere” — Jean-Martin Bauer, Director, Food Security and Nutrition Analysis, World Food Programme…(More)”
Article by Sam Freedman: “LLMs have become so pervasive in our lives that it is easy to forget how new they are. ChatGPT was launched in November 2022, and by May this year 63% of the British population had used it or a competitor. Compare this to take-up of the internet: five years after the public launch of the World Wide Web, just 9% of households had access.
The speed with which LLMs have become familiar and their ability to produce reams of serviceable text has led to rapid, large-scale shifts in behaviour that are overwhelming institutions unable to adapt quickly enough.
Universities are an obvious example: most had shifted to coursework-based examinations just before LLMs made cheating far easier. But the explosion of near-instantaneous text is causing problems everywhere. Employers face a tsunami of AI-generated job applications to be waded through, literary agents are bombarded with AI manuscripts, and complaints departments find themselves overwhelmed.
Government has the biggest problem. Modern administrative states are built around processes that are difficult to use, often deliberately so, which helps ration demand. Regulatory and legal routes are designed on the assumption that only a small number of people who might use them actually will. AI blows this assumption apart. The chart below shows twelve different government services from around the world and the surge in applications/complaints received – the grey line shows when ChatGPT was launched.
LLMs may not be the only reason for the rises, but are clearly playing an important role. One classification of requests to Google’s Gemini LLM finds that 1% are related to government interactions. That doesn’t sound like much until you realise that is around 2 billion a year for just one LLM. ChatGPT and Claude are more widely used.
The UK is particularly badly set up to deal with this, as we have an extremely centralised administrative state that is heavily dependent on national regulators and tribunals that can be quickly overwhelmed, and what local government we have is seriously under-resourced. In August, The Economist wrote about employment tribunals, which saw a 39% increase in people filing complaints against employers in the year to March. The backlog has shot up and hearings are now being listed to be heard in 2029, harming both unfairly treated employees and businesses. This is being repeated across government agencies…(More)”.
Article by Andrew Zolli: “Of all the questions we ask of a map, perhaps none is more fundamental than this one: Where are the people?
It sounds like a question we should have answered long ago. Yet in an age of seemingly ubiquitous, real-time information, our picture of where humanity actually lives remains surprisingly blurry. Censuses are expensive, infrequent and, in many places, badly out of date. Cities across the Global South are growing far faster than their residents can be counted. Conflict, disaster, and climate stress are setting millions of people in motion. The result is that many of the world’s most vulnerable communities are, in effect, invisible – and on a volatile, warming planet, invisibility can be fatal.
In early 2024, we announced a collaboration with the Institute for Health Metrics and Evaluation (IHME) at the University of Washington and Microsoft’s AI for Good Lab to help close that gap. Today, I’m delighted to share that the results of this multi-year initiative are being made widely available.
The result is Populace: a continuously updated map of the world’s population, realized in unprecedented detail. Populace measures the density of human populations across the entire globe at a gridded resolution of 40 meters, updated quarterly. It is the most up-to-date, highest resolution, and most accurate population data available anywhere – and it’s available now…(More)”.
Report by Trevor Potts: “Artificial Intelligence (AI) is rapidly reshaping how governments gather evidence, analyze information, and make decisions. In recent years, the Government of Canada has rapidly deployed new AI technologies and tools across the federal public service. While new technologies ostensibly promise improved efficiency and productivity, their deployment hasraised serious questions around perceived lack of transparency, gaps in regulation, implementation challenges, and potential for misuse and biased decision-making.
This project aims to explore how AI is reshaping evidence usage in the federal public service, and the implications AI usage has on federal policymaking and public trust in Canadian democracy…(More)”.
Article by Matt Prewitt and Salomé Viljoen: “AI companies’ need for large tracts of land has, for the first time, given communities a real public forum to push back against Big Tech. But stopping data centers won’t be enough. We must now extend the politics this moment has unleashed and give communities greater power over the future of a technology that will transform them.
AI models inherently concentrate power. They draw information from people and communities everywhere, and give their wielders power to fragment, shape, and manipulate those same communities. They are becoming extraordinarily powerful. Even if they aren’t existentially dangerous, they are not a “normal technology”.
Lawmakers must therefore do their job and regulate AI. A full response to AI’s harms will likely include a range of proposals. We believe one essential component of AI regulation is to move past interventions aimed at harm reduction to mechanisms of redistributing power over setting and directing AI’s future. In a new proposal, we argue for creating a new class of rights governing data use and collection, which makes associations — inspired by the historical structure of guilds, unions, and cooperatives — the wielders of a new class of data rights. These data associations can become the critical counterparties with whom tech companies must negotiate to build their powerful new technologies. We call this approach “associational data rights”.
This proposal aims not at “what” to fix about AI, but “who” to empower over the direction AI development takes. Our proposal creates new legal interests in data, which individuals exercise by assigning them to voluntary, member-governed associations. These associations would then work for their interests on their behalf. Large technology companies would have to negotiate with one or several of these associations in order to get rights to use data, or avoid serious liability.
Central to these new rights is the idea that individuals should be allowed to join and form a new type of special non-stock organization called a Data Rights Association (DRA). Being a member of a DRA would communicate to technology companies that certain rights concerning your data — the right to reasonable privacy, the right to a say in how the information is used, and the right to a fair share of the economic benefits — have been assigned to the DRA as your proxy. Companies would not be allowed to discriminate against DRA members in their service provision, or require terms of service that preclude DRA membership. In a nutshell, companies seeking to use customer data at scale would need to come to terms with a handful of consumer-side organizations with real bargaining power, instead of imposing lopsided terms on divided-and-conquered individuals…(More)”.
Book by Julian Posada: “Concealed behind digital platforms, a vast, dispersed, and largely invisible workforce quietly generates and annotates the data that powers today’s AI boom. In Platform Extractivism, information scientist Julián Posada argues that these platforms are engines of extraction rooted in the enduring social inequalities and transnational power disparities of coloniality. Posada reveals that technology, especially today’s so-called AI, is not artificial, autonomous, or intelligent; it inherently relies on and extracts from humanity. Drawing on mixed-methods research on three platforms in the Venezuelan data work sector, Posada exposes the human cost of this technology, revealing how digital platforms have capitalized on economic instability, targeting vulnerable populations to extract value from their precarious labor.
A critical intervention in the debate on the future of work, this book provides profound insight into the implications of artificial intelligence and moves beyond the context of advanced economies to focus on the labor involved in its production. Platform Extractivism questions whether AI is a tool for freedom or a motor driving the widening the gap between the unseen workers who teach machines and the corporations that profit from them…(More)”.
Book by Richard Cooke: “One of the keystone inventions of the twenty-first century, Wikipedia is the largest repository of human knowledge ever made, with editions in 343 languages, read by millions of people every day. Yet, its rightful status as a “digital wonder of the world” is coupled with an odd complacency. We all use Wikipedia (often without realizing it) but seldom think about what it is and where it came from. In this entertaining book, Richard Cooke offers a fresh and definitive account of this era-defining phenomenon.
The Last Best Place on the Internet reveals the personality clashes, philosophical disputes, private obsessions, pioneering innovations, and unexpected ideas behind Wikipedia’s fabled creation, extraordinary growth, and unparalleled success. Cooke examines the profound effect of an “encyclopedia anyone can edit” on politics, business, literature, and society at large. He explores the site’s shifting role in establishing fact amid deep uncertainty about truth and authority, the often hilariously fierce debates between “Inclusionists” and “Deletionists” about what matters, and the great perils Wikipedia faces today from bots, political division, and artificial intelligence…(More)”.
Article by Barbara Wheeler: “When the data-driven collective impact nonprofit, Baltimore’s Promise, set out to map the city’s youth programming, the numbers seemed to tell a clear story: For every four opportunities available to elementary school-aged children, there was only one for older youth, ages 19-24. So the fix seemed obvious: raise money, provide more programs for teenagers and young adults, done.
Before acting, Baltimore’s Promise took the finding to the people. Through its Youth Opportunities Landscape project, the organization held 19 group conversations that included 161 young people, parents, and caregivers. More programs would not have fixed the problem. Instead, Baltimore’s Promise learned that existing youth programs were often unavailable: Older youth described long bus rides of up to two hours to reach a program site. Many were parents themselves or primary caregivers for younger siblings with no way of arranging sibling care. Others were already working, and programs built around a school-day schedule could not accommodate them. The issue wasn’t just a supply or access problem; it was whether opportunities were available, realistically accessible, and aligned with what young people wanted and needed…(More)”.
Paper by Olivera Kotevska et al: “Scientific artificial intelligence (AI), spanning foundation models (FMs) to federated data-analysis pipelines, is becoming shared infrastructure across national laboratories, universities, hospitals, and industrial partners. This collaboration creates privacy risks whose natural unit is often an institution’s participation, research strategy, or technical capability rather than a single record. Differential privacy (DP), federated learning (FL), secure computation, trusted execution, and provenance each protect parts of the stack, but their guarantees rarely compose across mixed-trust institutions, access tiers, and autonomous agents. This perspective recasts privacy for scientific AI as an assurance problem defined by six elements: protected asset, observer, channel, permitted disclosure, guarantee, and evidence. We demonstrate the framing through a claim register for a composite cross-institutional scenario and use it to assess the model lifecycle. Two of the resulting gaps are specific to leadership-class facilities: scheduler, allocation, and telemetry metadata expose an institution’s resource posture, and instrument-attached control loops leak research strategy through timing and contention on shared accelerators. We identify six research priorities: institution-level guarantees, agent-communication privacy, cross-tier information flow, privacy-compatible reproducibility, leadership-scale accounting, and instrument side channels. The contribution is a common form for stating, comparing, and auditing claims whose guarantees otherwise remain fragmented across the scientific AI stack…(More)”.
Study by The European Parliament: “Generative Artificial Intelligence could strengthen open science by improving accessibility, findability, and usability. Still, under less favourable conditions, it could also weaken scientific exchange by obscuring provenance, deepening dependency on opaque infrastructures, and reinforcing inequalities and increasing synthetic overload, in which large volumes of AI-generated scientific content overwhelm the capacity of researchers and systems to verify, interpret, and trust information. This report describes how Generative AI is reshaping scientific exchange within the broader framework of open science. It shows that the main policy challenge is not whether Generative AI will be used in scientific exchange, but under what institutional, infrastructural, and normative conditions it can support reliable research, and knowledge valorisation. The European Union should treat scientific exchange as public-interest infrastructure and enact policies to support skills, strengthened provenance, transparency, reproducibility, inclusion, and strategic capacity in an AI-mediated research environment. This report provides specific policy options tackling the risks that can be associated with the use of AI in scientific exchange…(More)”.