Stefaan Verhulst
Article by Denice Ross & Chris Dick: “Federal data benefit American lives and livelihoods in ways most people never see, touching every corner of our lives. This includes a farmer pricing a crop, a county planning a hospital, a business siting a warehouse — all of these decisions use federal data. Other data save patients money by identifying generic drugs that can replace more expensive brand names, help airplanes avoid deadly bird strikes, and warn consumers about recalls of dangerous products.
Because these data are mostly invisible, their disappearance is invisible too — that is, until we need the data and they aren’t there anymore.
As federal data policy nerds, the question we get asked all the time is How much data has the current administration terminated?
The answer is that it depends on what we count as “data” AND what counts as a termination. That’s not a dodge – working through those two critical nuances is the substance of this piece.
The answer also depends on what the information will be used for. Ours is a data policy question, so we looked for structured, numerical datasets that have been terminated – meaning there will be no collections of those data in the future. Defining terminations that way lets us ask how agencies consulted with the public about a dataset’s value before ending it, and what its loss means for the federal government’s ability to serve the American people.
The purpose of the Federal Data Terminations Tracker is to be the most policy-relevant, verified accounting of federal data terminations available.
To create this Tracker, the dataindex.us team identified dozens of federal datasets and hundreds of data elements that have been terminated – significantly fewer than other reports, but more tailored to informing future data policies needed to run a modern society. These are data that have long underpinned policymaking, journalism, advocacy and research that improve American lives and livelihoods. These figures will change as terminations continue, collections are merged, and as court orders restore data.
Want more details on why this question about federal data losses is so tricky? Read on. Want to see what data have been terminated? Visit the Federal Data Terminations Tracker at dataindex.us/terminations-tracker…(More)”.
Article by Rachel Santarsiero: “…The fossil fuel industry is trying to weaken methane reporting requirements while simultaneously fighting to preserve the related federal emissions database it relies on for credibility, according to U.S. Environmental Protection Agency (EPA) records recently released in response to a freedom of information request.
When the Trump administration announced plans last year to repeal the federal government’s greenhouse gas reporting system, rather than celebrate, many oil and gas companies publicly urged the EPA to preserve it. But for more than a year, behind closed doors, the fossil fuel industry has been pushing to weaken one of the reporting program’s most consequential elements.
Oil and gas companies fear this Biden-era revision to the methane reporting rule known as Subpart W, would force them to disclose much higher — and previously hidden — pollution levels. Those concerns, brewing for years, grew into a wave that ultimately would break onto the friendly shores of the Trump EPA in response to the agency’s expected overhaul of both the methane reporting rule and the Greenhouse Gas Reporting Program (GHGRP), the most comprehensive system for tracking the nation’s greenhouse gases, industry comments and public records show.
At a private trade group meeting in 2023, one energy analyst said the revised methane rule would be a “major PR headache” for oil and gas companies. “How do we even go talk to our investors and explain that this is what’s happening?” he said. “Things that historically potentially had gone unreported will now have to be reported.”
In 2025, an industry consultant predicted that some companies could see their reported methane emissions increase by four to ten times under the rule’s new methodology. As recently as this spring, another consultant speaking to a gas group conference warned that updated disclosure requirements would increase reported methane emissions by roughly 16 percent. Last month, major gas producer EQT cited this revised rule as one reason its reported methane emissions rose in 2025…(More)”.
Paper by Atoosa Kasirzadeh & Iason Gabriel: “The creation of effective governance mechanisms for artificial intelligence (AI) agents requires a deeper understanding of their core properties and the implications they have for deployment. This paper provides a characterization of AI agents that focuses on four dimensions: autonomy, efficacy, goal complexity and generality. We propose different gradations for each dimension and argue that each dimension raises unique questions about the design, operation and governance of these systems. Moreover, we draw on this framework to construct ‘agentic profiles’ for different kinds of AI agent. These profiles help to illuminate cross-cutting technical and non-technical governance challenges posed by different classes of AI agents, ranging from narrow task-specific assistants to highly autonomous general-purpose systems. By mapping out key axes of variation and continuity across four dimensions, agentic profiles provide developers, policymakers and members of the public with guidance for effective AI governance…(More)”.
Book by Grace Huckins: “Science is in the midst of an under-recognized revolution. For centuries, prediction in science went hand in hand with understanding: knowledge of what advanced in tandem with knowledge of how and why. But in recent years, AI tools have enabled scientists to make predictions that previously would have been impossible, even if they don’t understand why those predictions hold true. Already, scientists have used these AI ‘oracles’ to design new drug candidates and help paralyzed people regain the ability to speak. These are consequential achievements. But they also raise a difficult question: If science can improve lives with prediction alone, should we still seek to understand the universe? In The Prediction Revolution, Grace Huckins, a trained neuroscientist and award-winning journalist, explores how AI is reshaping the relationship between prediction and understanding-and challenges us to consider what science is really for…(More)”.
Report by the Centre for Collective Intelligence (CCI): “How do people feel about devices that can read data from our brains, and about who gets to use the data they collect? Nesta’s Centre for Collective Intelligence, commissioned by the Information Commissioner’s Office (ICO), brought together a citizens’ jury to find out.
Neurotechnologies are devices that record signals from, or send signals to, the brain and nervous system. For years, neurotechnology has mostly been limited to medical uses such as cochlear implants or EEGs. Neurotechnology devices are increasingly moving beyond healthcare into consumer products for wellbeing, the workplace and education.
As this happens, it raises difficult questions about how the data derived from our brains should be protected. Developers currently face an uncertain regulatory landscape, and without clear guidance there is a risk of controversial practices or public backlash that could undermine trust in the technology.
What we did
Over two weeks in March 2026, 20 members of the UK public met for three online sessions. Guided by expert facilitators, they watched short informational videos, heard from experts, and, most importantly, deliberated together. The sessions moved from jurors’ first impressions to what they expect of regulators and developers, guided throughout by a single question: how do we make the most of the benefits of neurotechnology while managing the risks?
A citizens’ jury is well suited to a topic that is both technically complex and ethically contested. By giving a diverse group the time, information and space to deliberate, it produces considered judgements…(More)”.
Article by Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen: “We are releasing a revised version of “Canaries in the Coal Mine? Six Facts About the Recent Employment Effects of Artificial Intelligence.” Using payroll data from ADP, we document six facts about how employment has evolved since the release of ChatGPT, with particular attention to differences by age and AI exposure.
The updated data strengthen several patterns we first documented in August 2025. Most notably, the employment gap for young workers in highly AI-exposed occupations has continued to widen through mid-2026.
Our six main facts
- We do not see widespread, economy-wide job displacement associated with AI.
- However, young workers in AI-exposed occupations are increasingly falling behind their less-exposed peers. Employment among workers ages 22–25 in highly AI-exposed occupations now stands about 19% below where it would be if it had kept pace with employment among similarly aged workers in less-exposed occupations. Experienced workers show no comparable gap.
- This divergence has widened steadily since we first documented it in August 2025: by this same measure, the shortfall was 15% at the July 2025 data vintage and is 19% as of June 2026.
- The adjustment appears to operate primarily through reduced hiring of young workers rather than increased separations.
- The declines are concentrated in occupations where AI usage tends to automate human tasks. In occupations where AI is used more to complement workers, employment is flat or rising, particularly among more experienced workers.
- So far, adjustment is showing up primarily in employment rather than base pay…(More)”.
Paper by Jasper Roe and Mike Perkins: “Conversations regarding the societal implications of artificial intelligence (AI) and its integration into educational processes are ongoing. An area of great significance in this regard is agency, including how the use of AI technologies impacts an individual’s ability to exercise autonomy. In educational contexts, this remains a relatively unexplored yet vital subject. To shed light on this subject, this paper provides an exploratory thematic review of works which investigate the relationship between generative artificial intelligence (GenAI) and agency in education, mapping and interpreting the available literature through the lens of critical digital pedagogy (CDP). The findings suggest that while GenAI may enhance learner agency through personalisation and support, it also risks exacerbating educational inequalities and diminishing learner autonomy in certain contexts. This review also contributes to new sociotechnical perspectives on AI, highlighting how GenAI technologies may shape and be shaped by power dynamics and pedagogical ideologies. This review contributes to the existing debate on how AI technologies can be deployed in education and society responsibly by exposing how the use of these technologies may lead to unintended impacts, including the entrenchment of inequality and the reshaping of concepts of individual agency, authorship, and autonomy…(More)”.
Collected Essays by Moshe Maor: “…explains the phenomenon of sustained policy overinvestment or overreaction over extended periods. These long-term patterns of policy overproduction, known as policy bubbles, arise from psychological dynamics, institutional routines, and deliberate strategic action that generate positive feedback loops and lock policymakers into persistent overinvestment.
Leading expert Moshe Maor underscores the need to examine policy bubbles across their full life cycle: emergence, maturation, and termination or incorporation. Drawing on diverse case studies — including the NATO expansion foreign policy bubble, the corresponding political bubble in the U.S. Senate, and the U.S. crime policy bubble — the book traces the full life cycle of real-world policy bubbles and outlines how governments can better detect, understand, and respond to them. Through this analysis, Maor provides a comprehensive account of how policy bubbles persist and why uncovering their underlying drivers is essential for designing proportionate, evidence-aligned, and resilient policies.
Investigating a highly relevant policy anomaly, Policy Bubbles is an essential resource for scholars and students of public policy, public administration, and public management. Economists studying financial and asset bubbles, as well as government analysts, will likewise benefit from its groundbreaking insights…(More)”
Report by UNDP: “This report draws on workshops, stakeholder sessions, and in-country engagements with eleven countries and territories conducted through UNDP’s AI Trust & Safety Re-imagination Programme. It does not rank countries or propose a universal regulatory model. It surfaces what is already happening: the decisions being made, the risks materializing, and the governance approaches that are working under real institutional constraints.
The central finding is straightforward. Many of the most consequential AI governance decisions are not being made in policy frameworks. They are being made in procurement offices, vendor contracts, software update cycles, and the informal decisions of civil servants and institutional teams navigating AI use without clear guidance, approval processes, or accountability pathways. Getting governance right means embedding it in these operational settings, not treating it as a separate layer above them…(More)”
The Economist: “…the British state’s ability to function is about to get dramatically worse—a warning to rich democracies everywhere. Citizens frustrated by the poor deal they get from the authorities are turning to artificial intelligence to file objections and appeals, and claim their dues. The resulting deluge of complaints, and demands, will overwhelm bureaucracies built for the age of the post and the telephone. Too little is being done to prevent the state from drowning.
This inundation has been dubbed “agentic flooding”, and its tides are lapping at bureaucracies everywhere, from tax appeals to welfare claims to parking tickets. The waters are rising alarmingly fast in Britain. As we report this week, while worries about AI dwell on the threats to safety and jobs, the backlog in employment tribunals has quietly risen by 55% in a year, in large part due to AI-fuelled claims. Demand for emergency injunctions has surged 100-fold. And the AI tide has only just begun to come in….
At first sight this looks like a cracking result for fed-up citizens. No one likes parking tickets. The poor could exercise their lawful rights as successfully as the sharp-elbowed middle classes do today. Yet it threatens to become a tragedy of the commons. If the state is overwhelmed and cannot function, everyone loses. When 20th-century governments created broad rights, they had the noble ideal of making citizenship meaningful. The public would be heard in consultations, get information under transparency laws and win redress for maladministration from a panoply of ombudsmen, tribunals, commissioners and judges. But these analogue systems assumed that few people would have the time or temperament to pursue their rights to the bitter end; and that of those who did, few would have the money to pay for a lawyer…(More)”.