Stefaan Verhulst

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)”.

Article by Tai Lung: “Can you estimate someone’s cancer risk from the air they breathe? Not exactly. Every person has different genetics, lifestyles, occupations, and environmental exposures that change over time. Air pollution varies from day to day, season to season, and even block to block. But scientists can make remarkably reliable estimates of a neighborhood’s cancer risk based on the type and amount of toxic air pollution.

After a year-long delay, this April, EPA released the latest air toxics data, which only included raw air data downloads. This year, for the first time in nearly 25 years, the air toxics data did not include cancer risk estimates. 

Without a public explanation or opportunity for input, one of the nation’s most important environmental health datasets has quietly gone dark. 

The disappearance of EPA’s cancer risk data continues a broader trend under this administration of environmental, public health, and other government datasets becoming less available, less complete, or more difficult to access.  

What’s Changed?

For the 2021 data (this most recent release), only raw air emissions and concentration data are available for download. Previously, the EPA also produced detailed explanations and a mapping tool that made the data easier to discover, understand, and use. Additionally, they included cancer risk estimates to help translate data and complex scientific models into information the public could understand. 

Now, if users want to understand cancer risk from air pollutants, they will need to download and analyze detailed air concentrations of nearly 200 toxic air pollutants for more than 8 million individual locations…(More)”.

Paper by Theo Berger & Hannes Scheffter: “This study provides an innovative approach to identify the relevant competence profiles that data science education should promote for work in the social domain. We assess the supply side of European Data Science for Good (DSG) initiatives and analyse 335 projects. Based on project documentation, we identify 146 distinct data science methods and develop a taxonomy that distinguishes between statistical and machine learning approaches and classifies methods according to the scale of the target variables (nominal, ordinal, metric).

The results show a strong dominance of machine learning, particularly in classification-oriented problem settings, alongside a substantial role for exploratory and descriptive analysis in evaluation tasks. Drawing on these patterns, we provide a discussion on the implications for curriculum design of higher education programmes in social data science and artificial intelligence, arguing for an educational profile that aligns advanced methodological skills in machine learning with solid statistical literacy…(More)”.

Article by Gillian Tett: “Worrying about the state of America’s statistics might not seem that urgent, given all the other explosive political rows swirling in Washington. But voters should pay attention. For at the heart of this story are three key questions: can anyone paint an accurate portrait of what America is today? If so, how should this shape voting processes? And can this data be trusted in an era of manipulation, cyber hacks and artificial intelligence?

This matters. America’s founding fathers realised 250 years ago that you cannot create fair voting systems without measuring the population. So Article I, Section 2 of the Constitution states that Congress should carry out a census in “such manner as they shall by Law direct” every 10 years. Since 1790 census workers have always done that, crossing the country to count people, whether by horse, foot or car (or, in Alaska, on snow sleds).

The Constitution also stipulates that these census results should be used for “apportionment”, to divide the 435 seats in the US House of Representatives among the 50 states, based on each state’s population count. (The allocation in the Senate is permanently fixed.)

This makes it highly political. But it also matters economically: the census shapes how $1.5tn of state aid is distributed each year and is critical to business activity too. Civil rights activists argue this enables a fair apportionment and representation for minorities…(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)”.