Stefaan Verhulst

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)”.

Paper by Beth Simone Noveck, Nicholas Biddle and Alex Fischer: “Two trends are converging in countries around the world. Public anxiety about artificial intelligence is growing while communities satisfaction in how they participate and shape government is declining. Emerging examples from Bogotá, Hamburg, Brazil, Mexico and elsewhere show how AI can sharply improve the quality and experience of public engagement while reducing its time and cost. If done poorly or without safeguards, it can also introduce new risks. However, these can be managed with appropriate design, regulation and delivery by skilled practitioners.

This paper examines how artificial intelligence and digital platforms are changing what is possible, and suggests that a key opportunity is to build public-sector capability to deliver meaningful engagement.
The paper makes three claims: 

1. First, meaningful public engagement is a professional skill that requires practitioners to define clear goals, identify and reach appropriate participants, design effective tasks, interpret public input, and connect participation to decisions. Evidence from Australia and New Zealand suggests that public servants want these skills but rarely receive systematic training in them. 
2. Second, the very technologies driving many parts of public concern could also help governments listen and respond more effectively, make better decisions, and rebuild democratic trust. AI and digital platforms can reduce the time and cost of large-scale engagement, making forms of participation feasible that were previously difficult to sustain. The binding constraint is no longer cost or technology but capability to design and use. These technologies, however, need to be designed and used in ways that support democratic values and human judgment.
3. Third, realising this potential requires not only skilled practitioners but also appropriate digital infrastructure and regulation, alongside organisational support and demand.

The paper then explores opportunities for Australian institutions to teach a nine-step framework for AI-enabled engagement and sets out a research agenda to test whether such training changes practice. It introduces an approach to building these skills across public service and public-impact organisations, starting with a new free, self-paced online course developed by The GovLab and InnovateUS at Northeastern University, and Harvard’s Ash Center for Democracy Renovation, with input from more than 50 practitioners and experts across 24 countries. A pilot in Australia will follow the online course, testing how in-person workshops and skill-building can reinforce and apply those skills.

The paper concludes by recommending that governments invest in engagement skills, in platforms designed for democratic engagement, and in making public engagement a routine part of policy design, service delivery, and implementation…(More)”.

Article by Mehr Demokratie: “In autumn 2026, a randomly selected Citizens’ Panel will discuss measures to strengthen democracy in the European Union (EU). Until the end of the year, all EU residents can submit their questions and ideas on the subject online and at events organised on the topic… Perspectives on key aspects of democratic life, such as access to reliable information, media literacy, awareness of citizens’ rights, participation in decision-making, and the skills needed to engage in democracy in a digital world can be contributed. 

This debate follows up on the European Democracy Shield. It will feed into ongoing and future EU initiatives aimed at strengthening democracy and will inform future participatory processes, including a European Citizens’ Panel on Democratic Resilience. 

The Citizens’ Forum will meet from September to November 2026. Contributions from the online debate and events will feed into the Forum. The mini-public will discuss the contributions from the online debate and the in-person events and make recommendations to the European Commission….

From 22 May to 7 June 2026, the Sortition Foundation was on the road to recruit participants for the Citizens’ Forum. In collaboration with local partners across the EU, they knocked on doors and approached citizens from 150 randomly selected locations in all EU countries.

From the original pool of interested individuals, the final group of Citizens’ Assembly members will be selected at random to reflect the diversity of the EU population, taking into account gender, education, occupation and the spectrum between rural and urban areas…(More)”.