Stefaan Verhulst
OECD Report: “In the face of complex societal and technological transformation, governments are turning to incubators and accelerators to help their public institutions experiment safely, act quickly and embed more adaptive, forward-looking ways of working. This paper analyses 128 cases of public sector incubators and accelerators, looking at their capacity to help governments address institutional bottlenecks and better anticipate or respond to transformative change. The cases examined indicate that incubators and accelerators act as public sector innovation enablers that can support efforts to (re)design public institutions to better navigate complex challenges and evolving reforms. The paper examines how incubators and accelerators have been adapted to the public sector, considers common barriers and enablers, and suggests a blueprint for their design that comprises five key dimensions: strategic purpose, ecosystem engagement and co-ordination, organisational context, performance mechanisms, and value creation. Finally, the paper provides a step-by-step approach to help governments tailor these programmes to their own specific contexts…(More)“.
Article by Carolyn J. Lukensmeyer: “Americans’ disconnect from their elected officials has reached crisis levels. What potential do citizens’ assemblies and participatory decision-making hold for restoring people’s trust in their democracy? This essay explores three case studies of participatory governance at three levels—in California, in Bowling Green, Kentucky, and in Fort Collins, Colorado; virtually and in person—and finds that Americans are ready and willing to participate in well-designed deliberative processes and are willing to accept the outcomes of deliberative bodies. But representativeness and accountability in those processes are essential and not easy to come by. This analysis invites the question of whether these democratic experiments can scale up to where they are needed most: our national politics…(More)”.
Article by Eva Bordos: “Periods of major political transformation confront governments with an unusual combination of opportunity and responsibility. Democratic reconstruction after a period of backsliding, accession to the European Union, post-war reconstruction, or other moments of systemic change may require governments to undertake multiple large-scale reforms within a relatively short period of time. Constitutions may need to be rewritten, electoral systems reconsidered, public administrations reorganized, and healthcare, education, or social protection systems substantially reformed. These are not ordinary policymaking moments. They are periods in which countries make choices not only about individual policies and institutions, but about the kind of state—and, ultimately, the kind of society—they want to build.
Such moments present governments with two closely connected challenges. The first is a policy challenge: how can far-reaching reforms be designed so that they are workable, sustainable, and capable of being implemented in complex institutional environments? The second is a democratic challenge: how can societies be meaningfully involved in decisions that may reshape their institutions and collective future, generating democratic legitimacy and a sense of ownership over the direction of change? Large-scale reform, in other words, requires governments to think simultaneously about the substance of reform and about the democratic process through which reform is developed.
This democratic challenge is particularly important in societies where policymaking has historically been dominated by political, administrative, and professional elites and where citizens have had relatively few opportunities to participate meaningfully in public decision-making. Democratic renewal cannot be achieved solely through institutional repair or the restoration of electoral competition. Rather, renewal also requires rebuilding the relationship between citizens and public decision-making itself[1]. Moments of systemic transformation can therefore be understood not only as opportunities for institutional reform but also as opportunities for democratic development. Inviting citizens to participate in shaping major reforms can strengthen their capacity to engage with complex public questions, create a greater sense of democratic agency, and potentially contribute to rebuilding both trust in institutions and trust among citizens themselves. The question is therefore not simply what institutions and policies should be built, but how societies can be involved in building them.
Hungary today provides a particularly compelling setting in which to consider this question. For more than a decade, the country occupied a central place in political and scholarly debates about democratic backsliding. Under Viktor Orbán and his governing coalition, Hungary became one of the most frequently cited examples of contemporary democratic erosion, variously described as an illiberal democracy, competitive authoritarian regime, electoral autocracy, or hybrid regime. As the country enters a potentially transformative political period, however, a different question comes to the foreground: how should democracy be rebuilt after a prolonged period of democratic backsliding?..(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)”.
Paper by Levin Brinkmann et al: “Intelligent machines have the potential to uncover problem-solving strategies beyond human discovery. Emerging evidence from competitive gameplay, such as Go and chess, demonstrates that AI systems are evolving from mere tools to sources of cultural innovation adopted by humans. However, the conditions under which intelligent machines transition from tools to drivers of persistent cultural change remain unclear. We identify three key dimensions that modulate machine influence on human problem-solving: the discovered strategies must be non-trivial, learnable, and offer a clear advantage. Using a cultural transmission experiment, we demonstrate that when these conditions are met, machine-discovered strategies can be transmitted, understood, and preserved by human populations, leading to enduring cultural shifts. Conversely, using agent-based simulations, we show how machine influence is constrained in the absence of these conditions. These findings provide a framework for understanding how machines can persistently expand human cognitive skills and underscore the need to consider their broader implications for human cognition and cultural evolution…(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)”.
Report by Pew Research: “In November 2022, OpenAI released ChatGPT to the public for the first time. Less than four years later, around half of U.S. adults say they use chatbots powered by artificial intelligence, including 24% who say they use them daily. These tools’ ability to generate human-sounding text has raised a basic question about the modern web: How much content online is now written by AI rather than by other people?
To explore this question, we used the Common Crawl web archive to collect almost half a million English-language webpages from the past five years – starting a couple of years before the release of ChatGPT. We then ran the text of those pages through an AI detection tool called Open Pangram to see how many of them were likely written or substantially edited by AI…In the July 2026 snapshot, signs of AI authorship can be found in over one-third of pages published after ChatGPT was released. This is in line with other studies that have shown that large shares of recently published pages on the internet were likely written or substantially edited by AI…(More)”.
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)”.