Stefaan Verhulst

Article by Emanuel Maiberg: “After months of heated debate and previous attempts to restrict the use of large language models on Wikipedia, on March 20 volunteer editors accepted a new policy that prohibits using them to create articles for the online encyclopedia. “Text generated by large language models (LLMs) often violates several of Wikipedia’s core content policies,” Wikipedia’s new policy states. “For this reason, the use of LLMs to generate or rewrite article content is prohibited, save for the exceptions given below.” The new policy, which was accepted in an overwhelming 40 to 2 vote among editors, allows editors to use LLMs to suggest basic copyedits to their own writing, which can be incorporated into the article or rewritten after human review if the LLM doesn’t generate entirely new content on its own. “Caution is required, because LLMs can go beyond what you ask of them and change the meaning of the text such that it is not supported by the sources cited,” the policy states. “The use of LLMs to translate articles from another language’s Wikipedia into the English Wikipedia must follow the guidance laid out at Wikipedia:LLM-assisted translation.” I previously reported about editors using LLMs to translate Wikipedia articles and  Wikipedia editor, Ilyas Lebleu, who goes by Chaotic Enby on Wikipedia and who proposed the guideline said that it seemed unlikely the policy will last because previously the editor community has been divided on the issue…(More) “.

Book by Marina Nitze, Matthew Weaver, and Mikey Dickerson: “When the system breaks, what do you do? You’re in the middle of a meltdown. The platform is down, the phones are ringing, the headlines are brutal, and your team is looking to you for answers. The usual playbooks—careful planning, expert consultation, bold strategy—aren’t working. What if we told you that instead of the end of the world, this is your moment to create lasting, transformative change?     
 
Crisis Engineering is your field guide to leading through the chaos—and coming out stronger than before. Drawing on decades of experience inside some of the most complex systems in industry and government, Marina Nitze, Matthew Weaver, and Mikey Dickerson, of the crisis engineering firm Layer Aleph, reveal their powerful, hands-on framework for navigating high-stakes crises.
From the rescue of HealthCare.gov to wildfire response and pandemic logistics, this book offers real-world stories, practical tools, and hard-won insights into how complex systems fail—and how to help them recover. You’ll learn: 

• How to identify the 5 signals of a crisis—and use them to your advantage
• Why traditional leadership instincts fail under pressure—and what to do instead
• How to stand up your own crisis engineering effort when it matters most

Whether you’re in tech, government, healthcare, or any other critical system, Crisis Engineering gives you the mindset, tools, and vocabulary to lead with clarity and create lasting change…(More)”.

Research Agenda & Bibliography of Proposals by Anna Lenhart: “In recent years, academics, advocates, and policymakers have proposed or discussed the need for a new digital regulator (NDR) – a new agency of the federal government that regulates the AI and technology industry, with a particular focus on market competition, data privacy, and transparency & safety. We have documented over 20 academic papers and studies, think tank reports, books and parts of books, essays and op-eds, and pieces of legislation that propose such agencies or analyze such proposals. 

On February 25, 2026, the Institute for Data, Democracy and Politics at George Washington University and the Vanderbilt Policy Accelerator hosted many of the experts who authored those proposals for a day-long summit to discuss the need for an NDR and open questions related to the design of the agency. Informed by those discussions, this research agenda outlines questions we believe still deserve additional research attention, across disciplines. We are publishing this agenda in hopes to inspire scholarly work on these issues. Some areas may already have work that we have inadvertently missed from our literature review, and we welcome input from those interested in these issues…(More)“.

Paper by Adnan Firoze, et al: “Historically, only resource-rich U.S. cities have collected data about where their public trees are, usually through labor-intensive manual surveys or via coarse canopy-cover estimation. However, a significant portion of city trees are on private property, making them difficult to quantify with surveys, yet they contribute uniquely to species diversity and ecosystem service distribution. Further, canopy-cover estimation cannot provide information about tree density, locations of trees across different land types, or changes in tree counts. Cities are under continual change, and the mean mortality rate of urban trees is twice that of rural trees.Thus, frequent updating of tree analytics is critical for sustainable, habitable cities.

Method.  Recent advances in computing—in particular, generative artificial intelligence (AI)—have enabled our multidisciplinary team, spanning computer science, engineering, and forestry, to develop a first-of-its-kind computational method that can individually locate and maintain an inventory of trees in at least 330 U.S. cities (Figure  1). Using satellite data, this approach can complete the inventory process in less than a day of automated computing. Individual trees are challenging to discern in satellite images due to occlusion and resolution limitations, which in turn limits traditional segmentation-based approaches. Our approach leverages several key insights to enable a scalable generative AI solution. First, a frequent capture rate of satellite imagery (e.g., daily, monthly, etc.) provides spatiotemporal vegetation footprints, yielding richer information than single images. Our method includes a deep spatiotemporal vegetation cover classification using satellite images that classifies a city into tree, grass, and background, followed by a cluster-creation process and then individual tree localization using a set of conditional generative adversarial networks (cGANs). Further, our method can be applied to current or archived satellite imagery, allowing for change detection and historical analysis…(More)”.

Article by Sébastien Bourdon and Antoine Schirer: “On the Strava fitness app, there are individual cases that raise concerns, some of which Le Monde has already documented. For example, the naval officer who went jogging in the middle of the Mediterranean Sea, thereby revealing, at the moment of recording his run, the exact position of the Charles de Gaulle aircraft carrier strike group. Or the military personnel who exercised on the docks of the Ile Longue base, where France’s nuclear ballistic missile submarines (SNLE) operate, and whose activities provide information about patrol schedules. Or even the bodyguards of the French, American and Russian presidents, whose sporting activities make it possible to track these heads of state and, in some cases, to anticipate their movements.

And most significantly, Le Monde has uncovered several thousand servicemembers, of all ranks and specialties, who, by exercising and running in far-flung corners of the world and sharing their performances on their public Strava profiles, reveal the activities of the French armed forces as a whole, from the most banal to the most sensitive…(More)”.

Article by Ridhi Purohit, and Judah Axelrod: “Before building a new house or apartment building, residents and developers must first ensure their project adheres to local zoning rules. 

But zoning documents are often difficult for both applicants and local government staff to navigate. This can lead to extended back and forth between staff and applicants, slowing down the permit process at a time when most communities don’t have enough housing.

Some local leaders have expressed interest to Urban researchers in the promise of using generative AI to make zoning codes easier to understand, whether through screener tools that scan permitting applications to ensure they are complete, or chatbots that can answer development questions. However, leaders are hesitant to adopt tools that haven’t been properly vetted for the quality of the information they produce.

To test how well generative AI tools could interpret zoning codes, we ran a benchmarking exercise that evaluated the capabilities of various large language models (LLMs), building on previous work that explored whether machine learning could help automate the collection of standardized zoning data. To do this, we developed a set of zoning and permitting queries for Minneapolis—a city with a complex, 467-page zoning code and with zoning processes familiar to our team…(More)”.

Article by Karine Perset and Anna Pietikäinen: “…Six insights about AI regulatory sandboxes from around the globe

1. AI sandboxes are not uniform

• According to the Datasphere Initiative, three primary types of sandboxes are emerging worldwide, especially within the context of AI. Regulatory sandboxes: Collaborative processes where regulators work with innovators to test innovations under regulatory supervision.
• Operational sandboxes: Testing environments and infrastructure where data can be hosted and accessed in controlled conditions.
• Hybrid models: Combining regulatory oversight with operational capabilities, sometimes offering infrastructure and operational spaces for testing and experimentation (e.g., “supercharged sandbox” in the UK).

These models intervene at different phases of the policy and regulatory lifecycle. Some are employed before formal regulation to identify gaps and suggest necessary updates. Others operate during the development process, supporting iterative regulatory design. Some focus on helping understand legal obligations and ensure regulatory compliance, such as under the EU AI Act. Sector-specific sandboxes are also common, with countries adopting different approaches depending on regulatory priorities and institutional settings. Across these models, regulatory waivers are frequently used to enable experimentation under regulatory supervision. 

Several experimentation-related initiatives, such as regulatory testbeds, living labs, or policy prototyping, share certain features and objectives with regulatory sandboxes. What truly distinguishes sandboxes is that they are the most institutionalised form of regulatory experimentation, usually led by regulators and integrated with regulatory supervision..(More)”.

Article by Ian Leavitt: “Connecticut is one of many U.S. states increasingly using cross-sector data-sharing to improve public health outcomes, with the Prevention Data Portal a prime example of the state’s efforts. The portal demonstrates how state leadership buy-in, paired with expertise from nongovernmental partners, can overcome common barriers to cross-sector data-sharing and produce clear and meaningful information to help shape state policy—without requiring large new investments in data infrastructure or centralized control. Insights gained from Connecticut’s experience can inform the work of other state public health agencies as they look to expand cross-sector data-sharing through creative partnerships, simple use cases that show value early, and other avenues within their own states.

The Prevention Data Portal, launched in 2018 by Connecticut’s State Epidemiological Outcomes Workgroup (SEOW), houses data from local, state, and federal sources to advance health promotion and substance use prevention in the state. It provides free access to a wide range of data-driven products about populations in Connecticut, including epidemiological profiles, data stories using multiple streams of information, and infographics on mental health, substance use, suicide, gambling, and other public health topics. The portal is supported through federal block grant funding and partnerships between the SEOW and the Connecticut State Department of Mental Health and Addiction Services (DMHAS), the Center for Prevention Evaluation and Statistics (CPES) at UConn Health, and the Connecticut Data Collaborative (CTData).

In an ongoing collaboration with states to improve public health data, The Pew Charitable Trusts commissioned interviews with Connecticut officials and other participants in the conceptualization, creation, and use of the SEOW Prevention Data Portal. Those interviews produced several key takeaways…(More)”.

European Commission: “Open Research Europe (ORE) is the European Commission’s open access publishing platform for research funded by all EU programmes. Launched in 2021 to promote innovative, no-fee open access publishing, the platform is now preparing to enter a new phase.

Backed by a nearly €17 million budget for the period 2026-2031 and co-funded by the European Commission by up to €10 million, the new phase of ORE is set to begin operations as a collectively supported publishing service in the autumn of 2026, with CERN operating the platform. Leveraging on its success, the publishing service will be driven by national research organisations from 11 countries (Austria, France, Germany, Italy, the Netherlands, Norway, Portugal, Slovenia, Spain, Sweden, and Switzerland). In addition to researchers benefiting from EU grants, collective funding will also enable researchers from participating countries to publish without fees, thus expanding significantly author eligibility. ORE was conceived within the European Research Area (ERA)’s policy framework with the objective of ensuring open access to high-quality EU-funded research results, strengthening the free circulation of knowledge and maximising the impact of publicly funded research. In the five years since its launch, the platform has seen a steady growth and uptake among the research community, with more than 1,200 articles published and over 6,300 authors from more than 3,000 institutions worldwide.

The platform began its journey with a vision to redefine scholarly publishing, not only by offering researchers a no-fee open access platform, but also by enriching public knowledge and fostering trust in science through an innovative publishing model marked by rigorous and open post-publication peer review…(More)”.

Paper by Tae Wan Kim & Nicholas Vincent: “This paper asks a simple question: when we use digital platforms, are we more like horses that leave manure behind or like workers whose efforts help create value? Building on the idea of “data as labor,” we suggest that everyday activities, such as scrolling, clicking, and solving reCAPTCHAs, can function as a thin form of labor for data-driven firms. We argue that the key issue is not who owns the data, but whether the terms on which platforms use it are fair. To make this case, we connect debates on household production, unconscionable contracts, and surveillance capitalism to current data practices. We then explore how tools such as portability and erasure rights, data unions, and data strikes might give users modest forms of bargaining power. Our aim is not to offer a final theory, but to invite a more careful discussion of recognition and fairness in the data economy…(More)”.