Stefaan Verhulst

Article by Caitlin Hayes: “Researchers have created an interactive map identifying 1.8 million individual trees in New York City, with a new approach that could optimize the placement of trees for cooling in cities around the world. 

In the study, published Aug. 14 in Scientific Data, researchers identify the types of trees using satellite imagery as well as on-the-ground datasets and 3D data collected with lidar, short for light detection and ranging. The approach was 82% accurate overall and higher for common types of New York City trees. 

It’s the first “wall-to-wall,” citywide map identifying trees, and it includes trees on private property, in natural areas and other unsurveyed areas, which together constitute an estimated 65% of the city’s canopy and have not been accounted for on previous maps. The research team will use the map to determine which trees are best for cooling, and the data could inform a host of other planting decisions where the type of tree makes a difference, from reducing allergens to managing invasive pests. 

“If we’re going to be managing these urban forests well, we need to know what’s there, and now we know that for another 1.4 million trees beyond the street trees that were already surveyed,” said Daniel Katz, a senior author and assistant professor in the School of Integrative Plant Science in the College of Agriculture and Life Sciences. “We can now use this information to make sure we get the most benefits from trees, from cooling to reducing air pollution and flooding. We also hope people will enjoy looking at their own neighborhood and seeing which trees are around them.” 

The research is part of the Cool Trees project, led by co-author Dr. Arnab Ghosh, M.S. ’19, ’23, associate professor of medicine at Weill Cornell Medicine. Optimizing trees’ cooling effects is increasingly important, as heat waves in New York City become more frequent and intense. An unrelated Cornell project, “The Generative Canopy,” aims to leverage street-tree data to determine where more trees are needed to provide shade to vulnerable New York City residents.  

The city recently set a goal of expanding tree cover from 22% to 30% by 2040, and the new map could directly inform those efforts, along with broader research from the Cool Trees team to determine the relationship between tree density and type, temperature and heat-related health risks…(More)”.

Paper by Sachit Mahajan & Dirk Helbing: “As AI systems increasingly influence everyday life, integrating diverse community values is both ethically essential and practically urgent. This paper presents Value-Sensitive Citizen Science (VSCS)—a systematic framework that combines Value-Sensitive Design (VSD) with citizen science to support meaningful public participation in AI development. VSCS addresses gaps in the existing approaches by integrating culturally grounded methods and cognitive scaffolding through the Participatory Value-Cognition Taxonomy (PVCT). Community members engage as co-researchers through iterative cycles guided by extended scenario reasoning (What-if, If-then, Then-what, What-now), translating local values into actionable technical requirements. The framework also embeds governance mechanisms to ensure adaptability, accountability, and ongoing oversight throughout the AI lifecycle. By bridging participatory design with algorithmic accountability, VSCS challenges monocultural and top–down approaches to AI. We discuss practical implications, including power asymmetries, scalability, and epistemic justice, and propose strategies for policymakers and practitioners seeking to advance inclusive, value-driven AI design across diverse sociotechnical contexts…(More)”.

Paper by Aaron Witzki et al: “Access to sufficient and high-quality data has become a critical driver of innovation and competitiveness in today’s data-driven economy. However, many organizations, particularly smaller companies without an established user base, struggle to build or access adequately sized datasets. Yet, often a sufficiently large data base is not available. In this context, the concept of data ecosystems gained traction. Data ecosystems describe a distinct form of digital ecosystems in which data takes the role of the central product. Data ecosystems provide a potential solution to the need for data by facilitating the access. However, these ecosystems need to maintain the sovereignty of the data-generating, private consumer. It is of utmost importance to use or distribute data only with the consumers’ consent. Therefore, motivating users to share actively data with a data ecosystem is a relevant topic. For this reason, the aim of our work is to enable a more comprehensive understanding of the subject of incentive mechanisms fostering data sharing in data ecosystems. The study employs an extended systematic literature review to aggregate prior research in this domain, developing an extensive overview on the subject and highlighting future research avenues. We inductively identify five key dimensions of incentive mechanisms and five categories of influencing factors that may shape their effectiveness and users’ data sharing behavior. Building on these insights, we propose a conceptually grounded typology comprising eight ideal types of incentive mechanisms…(More)”.

(Open access) book edited by Riku Neuvonen and Jukka Viljanen: “We live in a world where many things happen digitally. Ones and zeros determine everyday actions and the course of life. The current digital world is two-dimensional. We see, hear and influence it using tools that are clearly part of the physical world. In the current digital world, we see things on screens. During this millennium, screens have become smaller, and in addition to computer monitors, virtual worlds can now be viewed on the screens of tablets and smartphones. Hardware and devices delivering augmented reality experiences are already on the market, as are virtual reality glasses. Headsets of different types are already commonplace.

A real virtual world is one that we experience like the physical world. In this book, we imagine the potential of such worlds and we examine these from the perspective of law and related disciplines. The current digital world has led us to questions about the applicability of rights. In theory, the regulation of the physical world also applies to the digital world. In practice, this is not always the case, as problems associated with the protection of privacy but also disinformation, online hate and others have emerged. In this context, we can talk about digital human rights, digital rights and digital constitutionalism.

The problems we face in the digital world are related to access, access to information, and privacy. The virtual world has the same problems, but virtuality as an all-encompassing experience also creates new challenges. In the age of platforms, algorithms have played a key role in displaying content and users are now shown what they or advertisers think they want to see. Various forms of artificial intelligence are also involved. These opportunities and problems of the platform era will also transfer to the virtual worlds of the future. This is especially the case when the advocates of virtual worlds, such as the metaverse, want them to be worlds where users spend a significant portion ofp. 2their time. These questions will be approached and explored from several different starting points.

This book is about considering, imagining and reflecting on virtual worlds. In this introduction, we will establish a picture of what virtual worlds can be, largely based on entertainment, films, books, television series, and games. This basis is logical since these forms of entertainment have been creating and experimenting with virtual worlds for decades. After establishing this picture, we will provide an overview of the research that deals with rights in virtual worlds before introducing the authors of the chapters and how each will approach this challenging theme from different perspectives…(More)”.

Report by the U.S.-China Economic and Security Review Commission: “China watchers in 2021 would not have predicted the country would lead the world in commercializing data and treating it as an asset. After regulators in 2020 abruptly canceled the mega-IPO of Ant Group, then one of China’s largest and most prominent fintech firms, China’s homegrown tech giants spent the next three years in the crosshairs of the Chinese Communist Party (CCP). Curbing the private sector’s ability to collect and use data without state oversight was one focus of the tech crackdown, with the Cyberspace Administration of China (CAC) targeting technology firms and social media platforms. In those same years, China stepped up censorship of economic data, canceling official series and banning private estimates that painted an unflattering picture and limiting foreign access to Chinese data and its aggregators. 

Yet as it was reining in big tech’s data practices, China’s government was figuring out how to extract value from data at a national scale. Although Chinese officials designated data as a factor of production in 2020, China’s first significant action to reopen space for commercial innovation and data monetization policies came in 2022 with a sweeping development framework. Fast forward to 2026, and China’s data economy is beginning to flourish through government-led data exchanges, new data accounting rules, and pilot programs that treat data as a resource. As more actors refine and package data, they unlock productivity gains for themselves or outside buyers, much like land or capital goods are created, used, and transferred. 

Rather than leaving the development of a data economy up to market forces, China is building infrastructure where participants can exchange data—fostering third-party services like data valuation, analytics, and financing and encouraging wider participation in the data economy…(More)”.

Article by JP Flores and Hannah Frank: “…The deeper problem is that a system built around journal prestige shapes what science gets done, how fast it moves, and whose work gets seen. 

We come to this argument from different directions. One of us, Hannah, conducts soil research on vineyards. Fundamental to the project are the relationships she has built with grape growers and winemakers. Yet when her work is finally published there is no guarantee those growers will be able to easily access the material, or that the wine industry beyond the academic paywall will be able to take the findings and apply them more broadly.

The other, JP, has committed years to experiments, failed analyses, and revised manuscripts that often seemed to be judged less by what they contributed to science and more by which journal ultimately agreed to publish them. Like many scientists, he was then asked to pay over $10,000 in article processing charges just to make that research publicly available. That money could have funded follow-up experiments, a student’s salary, or an entirely new project.

There are two central critiques of current publishing venues. First, systems that reward publication volume can encourage researchers to pursue smaller, lower-risk projects that generate a steady stream of papers rather than tackle more ambitious questions whose answers may take years to emerge…(More)”.

Climate Adaptation Knowledge Base by Urban Tech Hub: “Climate adaptation is a trillion-dollar challenge.Today the world spends $190 billion a year defending 1.2 billion people to developed-economy standards. Protecting everyone exposed to climate hazards would cost $540 billion…We’ve cataloged data on 2,667 adaptation actions, 831 solutions, and 12,868 outcomes in 1,309 cities globally…(More)”

Book by Jeff Jarvis: “The Linotype mechanized the 400-year-old process of setting type one laborious letter at a time, and thus ignited an explosion of newspaper, book, and magazine empires. The technology helped transform Mark Twain into a premier literary celebrity, but also cost him his fortune — as well as his sense of humor and optimism. The Linotype’s era was a bridge between Twain’s Gilded Age with its tycoons of steam, steel, and wire and today’s Gilded Age with its barons of bits and AI…(More)”.

Report by Pew Research: “Americans have become increasingly worried about artificial intelligence over the years, and young adults’ concern has continued to climb. Worry over job loss, too, is on the rise…

Americans have grown more concerned about AI over time

% of U.S. adults who say the increased use of artificial intelligence (AI) in daily life makes them feel …

Source: Survey of U.S. adults conducted June 22-28, 2026.

Today, 52% of Americans say they are more concerned than excited about the increased use of AI in daily life – up from 37% in 2021. Another 9% are more excited than concerned and 37% say they’re equally excited and concerned, according to a Pew Research Center survey conducted June 22-28, 2026.

Concern about AI is up among younger and older Americans alike since we first asked this question in 2021. But while the rise was mainly in the first two years for Americans ages 30 and older, concern continues to climb for adults under 30 – whose skepticism of AI has made headlines in recent months…(More)”.

Article by Urs Gasser, Viktor Mayer-Schönberger, and Fabienne Marco: “The current approach to artificial intelligence oversight is largely built on measurement. Benchmarks assess capability, red teams probe for failure modes, and evaluation frameworks certify safety and legal compliance before deployment. These instruments can be valuable, but they also share a critical structural vulnerability that AI governance has not yet adequately absorbed: The measurements used to verify AI models are not external to the objects being measured. Rather, this program of measurement operates within the same ecosystem—and is shaped by the same competitive pressures and institutional incentives—that produces the AI technologies it is meant to assess. A result is that the measured behavior of a model may diverge significantly from its behavior when deployed.

Put simply, as AI systems and the organizations building them learn what evaluators look for, the AI model performs for the test, figuring out how to excel in benchmarks without necessarily becoming safer, more useful, or more reliable in real-life scenarios. As evaluation increasingly assesses only the model’s capacity to ace that same evaluation, the boundary between system and oversight grows porous.

This kind of situation is known as a measurement trap: When a measure becomes a target, it ceases to be a good measure. Measurement traps are not unique to AI. Standardized testing in education leads schools to “teach to the test” rather than help students develop critical thinking skills. In software development, when productivity is tied to the number of lines of code written, programmers write long, repetitive code, which may or may not be good software. And in business, when bonuses are tied to revenue measures, managers prioritize short-term sales over long-term profitability…(More)”.