Stefaan Verhulst
Article by Jason Koebler: “The Trump administration is using an anti-drug trafficking grant program from the 1980s to force cities around the country to funnel their ongoing collection of automated license plate reader data into large federal surveillance centers that fall under the White House’s jurisdiction, according to public records and court documents reviewed by 404 Media.
The records show the workings of a complex system in which the federal government is using the “High Intensity Drug Trafficking Area” (HIDTA) program to obtain data from local cops’ Flock, Axon, and other ALPR cameras and aggregate it on federal government servers, where it is then accessible to other federal, state, and local law enforcement agencies. In some cases, data that goes to HIDTA is then also shared to a larger program run by the Drug Enforcement Agency called the “National License Plate Reader Program” (NLPRP).
Through HIDTA systems, local license plate data can be accessed by various federal agencies, including those who may not have contracts themselves with the ALPR companies. The program raises significant privacy and transparency concerns; as cities begin to place restrictions on how their Flock data can be used, it represents an end-around for federal law enforcement to access data that it might not normally be able to, and to do so without any obvious oversight body. HIDTA falls under the jurisdiction of the White House’s Office of National Drug Control Policy (ONDCP), and it recently won an award from the Trump White House for “managing an automated license plate reader platform that brings together all levels of law enforcement.”
Many of the documents used in this article were obtained using public records requests by Cris van Pelt, the creator of the website HaveIBeenFlocked.com and its associated investigative blog, Footnote4a. “When local police and private vendors promise community control over surveillance, they obscure a broader agenda. The DEA and ONDCP are already aggregating local data behind the scenes,” van Pelt said. “This bypasses democratic processes entirely. The public has made it clear that mass surveillance violates fundamental rights, even when it is laundered through fragmented systems, private companies, and regional task forces.”..(More)”.
Report by The World Bank: “Digital technologies have significant potential to accelerate progress toward the Sustainable Development Goals, yet many government digital services fail to achieve widespread adoption because they are designed around administrative processes rather than user needs. This report examines how behavioral barriers create a “lost path” for users, resulting in low awareness, poor uptake, high dropout rates, and limited repeat use of digital services. It introduces Behavioral User Journey Mapping (BUJM) and the COM-B Model (Capability, Opportunity, Motivation, Behavior) as practical tools for identifying and addressing user pain points throughout the service journey. Drawing on case studies from around the world, the report demonstrates how behavioral science can improve service engagement, increase completion rates, and enhance user trust. It concludes with recommendations for user-centered design, iterative testing, inclusive service development, and behavioral performance metrics to help governments successfully close the “last mile” gap in digital government…(More)”.
Handbook by the United Nations Democracy Fund: This handbook is a ‘how-to’ guide for commissioning, designing, and operating citizens’ assemblies to achieve a more informed, substantive, and representative way to make trusted democratic decisions. While there are many ways to consult the community, the focus of this handbook is on deliberative approaches, of which the citizens’ assembly is the best-known model. Citizens’ assemblies combine civic lottery (also known as sortition or selection by lot) with deliberation to bring a diverse group of people across ages, genders, locations, and perspectives together in an environment where they learn, think, discuss, and work toward finding common ground to address a given problem.
An assembly aims to provide commissioning authorities with a set of recommendations that assembly members have agreed to, supported by their reasoning and evidence. This handbook walks you through each step of the journey: identifying when a deliberative approach is appropriate, designing the key features of a process, preparing and running the assembly itself, and understanding how to translate recommendations into action…(More)”.
OECD Report: “People use public administrative services (PAS) frequently, for example when registering a birth, or getting an ID, license or permit. During important life events, like having a baby or moving home, people often use many different PAS. The quality of these services affects people’s lives and their trust in institutions. Serving Citizens 2026 presents an overview of how PAS are organised and delivered in OECD and partner countries. It provides guidance on how governments can make these services more accessible, responsive and proactive for the people who use them.
Key figures
87% of OECD countries have a strategy to improve public administrative services
55% of OECD countries use performance data or user feedback to improve services
Only 20% of common life events are supported by integrated services across different public agencies..(More)”.
Article by Juliana Castro Varón and Dylan Freedman: “Artificial intelligence is changing the world, and the humans in it, in ways we don’t yet understand. People are increasingly worried that if we rely on chatbots for thinking, our brains will become slower and less engaged. Is A.I. making us stupider? Scientists, too, want to know.
A team of researchers from the University of California, Irvine, and the education company McGraw Hill examined a decade of data from a popular online learning tool by McGraw Hill, starting in 2015. Across millions of math sessions by a group that ranged from fifth graders to college students, the researchers compared performance from before 2022, when ChatGPT first came out, with the work that followed its arrival…(More)”
Article by Pew Research: “As artificial intelligence becomes more advanced, there is growing interest in using it to stand in for real respondents in public opinion surveys. Put simply: Instead of contacting large numbers of people and asking them what they think, pollsters can ask an AI model to predict how those people would have answered a certain question.
Pew Research Center has long sought to better understand new developments in public opinion research, and we wanted to learn more about how this AI-based polling works. So we ran an experiment. We developed a state-of-the-art process for fielding AI surveys and compared their results with three recent survey waves from our American Trends Panel (ATP) taken by human respondents at roughly the same time.
This experiment taught us that, at this time, AI models are not an adequate replacement for traditional polling on topics of broad public importance. Our AI-generated survey results struggled to accurately reproduce the findings from high-quality public opinion polls in a variety of ways.
Here are some of the main issues we encountered:
Results of AI polls differed – often by quite a lot – from results of human surveys
Across nearly 300 individual survey questions, the estimates produced using our AI respondents differed from their human counterparts by an average of 12 percentage points...(More)”.
Article by Andrew Stokols: “In April 2025, twenty-one humanoid robots lined up beside human runners in Beijing’s Yizhuang district for what organizers billed as the world’s first humanoid half marathon.1 Most didn’t finish. Several fell over at the start. The coverage abroad ranged from hyperbolic to dismissive: evidence of Chinese technological mastery or a performative spectacle designed to impress the world.
China’s technology landscape is indeed layered with infrastructures and buildings built primarily to be seen: exhibition halls, demonstration zones, nighttime drone shows, smart-city command centers with wall-sized screens. Chinese has a term for the tendency, 面子工程 (mianzi gongcheng), “face engineering,” and in 2025 Xi Jinping himself criticized wasteful vanity projects.2 But to stop there misses what these projects do. A robot race on a closed urban course is also a test: of locomotion over real pavement, of battery endurance, of how machines behave among crowds. Yizhuang, not coincidentally, is also where Beijing’s autonomous vehicle testing zone has spent five years accumulating edge cases on public roads.
Chinese policymakers have a word for what connects these things, and it has become one of the most important concepts in the country’s industrial policy for artificial intelligence: 场景, changjing, or “scenario.”
In Chinese policy vocabulary, a scenario is a bounded, real-world environment in which an emerging technology can be deployed, observed, and improved before it is commercially viable. “Scenario innovation” (场景创新) and “scenario opening” (场景开放) entered official usage in the late 2010s, and in July 2022 six central agencies, led by the Ministry of Science and Technology and including the Ministry of Industry and Information Technology, issued the Guiding Opinions on Accelerating Scenario Innovation to Promote High-Quality Economic Development through High-Level AI Application..(More)”.
Paper by Alan Chan et al: “In contrast to even a year ago, AI systems now write most of the code inside the companies that build them. As more of the AI research and development (R&D) pipeline is automated, could AI progress radically accelerate in an “intelligence explosion,” where years of advances are compressed into months or less? Preliminary evidence suggests that it could. In this work, we assess this evidence, analyze an intelligence explosion’s potential impacts, and propose policy responses. AI systems are on track to automate most AI R&D work within a few years, and possibly all of it. If this triggers an intelligence explosion, it could dramatically bring forward AI’s benefits, but also pose extreme risks: capabilities growth could accelerate far beyond what society can keep up with, humanity could lose control over superhuman AI systems, and checks on power within and between states, companies, and branches of government could be severely eroded. Although there remains much uncertainty about these possibilities, the high stakes warrant serious further attention. Policymakers should urgently obtain more visibility into the automation of AI R&D, develop ways to steer and constrain an intelligence explosion, and prepare society to adapt to an intelligence explosion’s impacts…(More)”.
Press Release: “The U.S. National Science Foundation announced today the launch of the NSF Open Knowledge Network (NSF OKN), a national open-data infrastructure that links 43 interconnected knowledge graphs and tens of billions of connected facts across health, the environment, justice, manufacturing, and national security. The network is live and open to the public at okn.us.
NSF OKN provides a structured, persistent, verifiable knowledge layer to complement contemporary artificial intelligence models, providing grounding of facts, attribution, and knowledge governance essential to critical AI applications in a number of fields. OKN’s value goes beyond machines querying structured knowledge: Its deeper value provides AI systems with a shared, explicit, interoperable, and auditable representation of the world on which they are expected to reason and act…(More)”.
Paper by Maojun Sun et al: “In recent years, data science agents powered by Large Language Models (LLMs), known as “data agents,” have shown significant potential to transform the traditional data analysis paradigm. This survey provides an overview of the evolution, capabilities, and applications of LLM-based data agents, highlighting their role in simplifying complex data tasks and lowering the entry barrier for users without related expertise. We explore current trends in the design of LLM-based frameworks, detailing essential features such as planning, reasoning, reflection, multi-agent collaboration, user interface, knowledge integration, and system design, which enable agents to address data-centric problems with minimal human intervention. Furthermore, we analyze several case studies to demonstrate the practical applications of various data agents in real-world scenarios. Finally, we identify key challenges and propose future research directions to advance the development of data agents into intelligent statistical analysis software…(More)”.