Stefaan Verhulst

Paper by Daniel Berliner: “Participatory institutions often aim to yield information useful to policymakers, whether about public preferences, problems, or solutions. But how can large numbers of public contributions be processed into interpretable and actionable information outputs? As theorists and practitioners increasingly call for participatory institutions to operate at larger scales, often enabled by new technologies, this challenge only becomes more important. This article reviews recent work on participatory institutions in order to develop several insights: (a) that there are different types of information that policymakers may aim to learn and that are relevant to different policy stages; (b) that information must be effectively processed in order to be interpretable and actionable for policymakers; (c) that there are different types of information processing, depending on the specificity and novelty of the information outputs that policymakers aim to learn; and (d) that there are different ways in which this processing can be delegated, whether to experts, ordinary people, or automated algorithms. Better recognizing these differences will help both researchers and practitioners better understand the potential and the limitations of participatory institutions in different settings and with different goals…(More)”.

Article by Heather Pearson: “A UK survey published in January found that only 40% of people think that science information they hear is “generally true”. Another global poll showed that 70% of people believe at least one false or unproven claim, such as that the risks of childhood vaccines outweigh the benefits.

In the United States, President Donald Trump and his administration are using the idea that science is not trustworthy as one reason to cut research budgets, reject evidence-based medical advice and exert unprecedented political control over research. “Over the last 5 years, confidence that scientists act in the best interests of the public has fallen significantly,” said Trump in an executive order last year.

Even the Vatican is voicing concern. This September, a meeting at the Pontifical Academy of Sciences will examine how “the crisis of trust in science has become a pressing issue”.

But is trust in science really that weak? Researchers studying this have reached some surprising conclusions. From a global perspective, public trust in science and scientists is high, they say. One of the largest studies, which surveyed nearly 72,000 people across 68 countries in 2022–23, reported a “moderately high” average trust score of 3.6 out of 5. “The idea that there’s a generalized, pervasive lack of trust in science and experts is just completely unfounded in my mind,” says David Bersoff, head of research at the Edelman Trust Institute, a think tank in New York City…(More)”

Press Release by Google Research: “Approximately 500,000 deaths every year are attributed to extreme heat, a crisis intensified by the urban heat island effect, which causes metropolitan areas to warm at double the worldwide average. Earlier this month, record-breaking heat waves across Western Europe pushed temperatures past 40°C (104°F). The prevalence of heat-trapping materials, like dark pavements and roofs, combined with a lack of vegetation, largely drives this localized warming. Heat mitigation measures are critical to reducing this toll, and cool roofs offer a highly cost-effective solution. By increasing rooftop reflectivity (albedo), we can significantly reduce the amount of solar energy absorbed by buildings, ultimately lowering local surface temperatures and protecting vulnerable communities.

To address this, Google Research is building AI-driven tools to help lower city temperatures and keep communities safe. By applying AI to high-resolution satellite and aerial imagery, our Heat Resilience tools help cities quantify the impact of targeted cooling interventions. In 2024, we piloted this approach with 14 cities, providing them with rooftop reflectivity data to identify highly vulnerable neighborhoods and determine where cool roofs would yield the greatest temperature reductions. This data guided critical decisions across several cities, resulting in initiatives such as cool roof ordinances and adaptation plans.

Now, we are scaling this impact. In “Estimating high-resolution albedo for urban applications“, published in Nature Communications, we detail our methodology for mapping building-level reflectivity across diverse urban environments. This research bridges the gap between general climate observations and actionable, building-level data. We are also releasing an expanded albedo dataset covering over 50 global cities to empower urban planners worldwide to prioritize cool-roof interventions. This dataset is open and accessible through our new, high-resolution Heat Resilience Earth Engine App…(More)”.

Book by Michael Clarke, Manuel Garcia-Garcia, and Michael Joffe: “When a chatbot lies about an airline’s bereavement policy, who is to blame? When an AI-generated painting wins a state art competition, what does it mean to be a creator? Our relationship with artificial intelligence is not just technical; it’s profoundly human. Smarter Together is your essential guide to the hidden psychology behind the AI revolution. Drawing on insights from neuroscience, behavioral science, and their popular NYU courses, the authors reveal how intelligent systems are designed to mirror our thinking, feeling, and decision-making. Through unforgettable case studies, this book unpacks the new equations of trust, the cognitive biases that shape our choices, and the cultural forces defining AI’s promise and challenge. Moving from theory to practice, it provides a vital toolkit for designing and marketing AI products that augment, rather than replace, human intelligence…(More)”.

Report by Nathan Goldschlag: “How many firms are using Artificial Intelligence? What are they using it for? How many workers are using AI, and how are they using it? 

To track and understand the effects of AI on the economy, researchers will need accurate, detailed, comprehensive answers to these fundamental questions. 

Partial answers won’t do — not at a time when policymakers are struggling to catch up with the sweeping consequences of AI for workers and businesses. Without improved measurement, they risk getting the policy response wrong. 

Fortunately, the statistical infrastructure is already in place to help get it right. But that infrastructure needs an upgrade, fast, to match the scale of the challenge. 

In this essay, EIG’s Nathan Goldschlag outlines the necessary investments in the U.S. statistical agencies that will give them the ability to answer the most pressing and vital questions about the impact of AI on the American economy…(More)”.

Handbook by Cathy Riley et al: “…provides an in-depth guide to planning and sustaining a Mobile Phone Data (MPD) initiative, with a primary focus on the use of Call Detail Records (CDRs) for public policy, statistical, and development purposes, including operational decision-making. It builds on, and develops further, the concepts and principles first described in the original Handbook on the Use of Mobile Phone Data for Official Statistics released by what was then known as the UN Global Working Group on Big Data for Official Statistics. (United Nations Statistics Division 2019)

The handbook is intended for practitioners working in national statistical offices, telecom regulators, mobile network operators, government ministries, and partner organisations who would like to initiate an MPD initiative. It also contains advice and guidance for those who may already have embarked on the journey of establishing such an initiative but who are searching for more information or guidance on how to do so effectively and sustainably. It is designed to enable such readers to understand not only the steps involved in planning an MPD initiative, but also the technical, institutional, legal, and ethical reasoning that underpins each decision. It is suitable for both technical and non-technical audiences, and does not assume deep prior technical expertise in MPD analytics…(More)”.

Report by DARE UK (Data and Analytics Research Environments UK): “…offering a detailed, UK-wide picture of how Trusted Research Environments (TREs) are supporting research for public benefit.

Building on early insights shared late last year, the full report brings together findings from a 2025 survey of 63 organisations across universities, government, charities and the private sector. It provides one of the most comprehensive overviews to date of how TREs operate, how they are funded and how they are evolving to meet growing demand.

Enabling research while protecting privacy 

The review highlights the central role of TREs in the UK’s approach to using sensitive data responsibly. These highly secure computing environments allow approved researchers to analyse sensitive datasets without the data leaving a controlled setting.

TREs make it possible to carry out vital research using data from areas such as health, education and social care, while maintaining strict safeguards and public trust.

DARE UK’s work focuses on strengthening and connecting these environments to support trustworthy, consistent and high-quality sensitive data research in the UK.

A growing and increasingly capable ecosystem

The review confirms that the UK has a large and expanding TRE ecosystem. The organisations surveyed together support nearly 7,000 active research projects per year using sensitive data, demonstrating the scale and importance of this infrastructure.

Most activity sits within universities and the public sector, with TREs operating across all four UK nations, although capacity and capability vary between regions.

The review also shows that many organisations perform multiple roles across the system, reflecting the collaborative and interconnected nature of sensitive data research…(More)”.

The  Preliminary Report of the Independent International Scientific Panel on AI: “…a first-of-its-kind independent scientific assessment of the capabilities, emerging opportunities and risks of artificial intelligence. The Panel, composed of independent scientists and experts from all 5 UN regions, outlines trends in AI. It’s central warning: current safeguards cannot keep pace with the growth of AI’s capabilities.

It identifies a crucial evidence challenge for decision-makers around the world: policymakers need scientific evidence to effectively govern AI, but by the time the evidence is clear, it may be too late to act on it. In the report, the Panel outlines its findings across seven key domains:

• AI science, advances & trajectories
• Societal applications: science, health, education & agriculture
• Economic implications
• Security, systems & environmental implications
• Human rights, information & democracy
• Cultural & individual flourishing, autonomy and child safety
• Management, governance & reliability..(More)”.

Map by Current AI: “By nature, all AI systems are opaque and multidimensional. They become even more complex when thousands of developers from all over the world contribute unique projects across different layers. This is the current state of the open source AI stack: seriously robust, but fragmented, duplicative, and hard to see as a coherent whole.

The Gap Map is a living, actionable visualization of AI’s open source landscape.

Building on work from leading open source AI experts at the Columbia Convening, the Model Openness Framework, Hugging Face, and others, it comes out of cumulative work to identify the points of highest leverage in the stack: where to build new, where to invest in capability, where to open up the tools. By creating an up-to-date visualization of the ecosystem where we can all see both the progress and the gaps, we can rally the community around a collective roadmap.

We intentionally don’t compare closed versus open AI ecosystems, or point to where open source AI leads or lags. Instead, the Gap Map illustrates what’s needed to build the system we want.

For details on how products are scored and categories assessed, see our methodology…(More)”.

Paper by Elettra Bietti: “The ability to direct and receive attention is constitutive of human life. Humans have an inborn need for attention, and an inborn ability to direct attention for survival. Yet attention is not just a creature of an individual’s mind. It is a relationship between people and their environment. As such, our attention is shaped by the material, social and economic conditions that surround us. Today, people’s attention is increasingly extracted and colonized through technology. Attention platforms and AI technologies are transforming the shape, objects, metrics and value of human time and attention.

This article focuses on the role of data-attention platforms in transforming time and attention. Data-attention platforms include social media platforms such as Facebook, YouTube, TikTok, and increasingly AI companions such as Replika or Character.AI. They capture data and attention and draw revenues from them, primarily but not exclusively through surveillance advertising. The business models of data-attention platforms are organized around the data-attention imperative, the drive to continuously capture troves of data and attention to generate value. They capture eyeballs to sell ads and collect data to target ads and maximize engagement. Time online enables more data collection, which, in turn allows for the design of products that more effectively addict users. This extractive data-attention spiral produces a harmful commodification and erosion of time and attention which shrinks the human experience and undermines collective life.

This article asks how governments should and shouldn’t regulate data-attention platform business models and the distortions they cause. It is tempting to reduce growing data-attention disorders to problems of individual choice online, delegating solutions to market-based tools, more competition or the exercise of individual data protection rights and parental controls. Instead, the answer requires moving past individual preferences and embracing an infrastructural approach focused on changing platform incentives and technological affordances and on safeguarding space for offline time. Privacy and data protection, child social media regulations and productivity tools provide for controls and safeguards that too often magnify instead of addressing attention disorders. The idea of individual autonomy that underlies them is unfit for the attention era. The article advocates a conception that takes the power of platforms to shape our attention seriously and advocates for the protection of children and adults’ time away from technology. Time away from technology is a collective good in need of protection. Based on a three-fold agenda that incorporates design changes, taxation, and legal reform to reduce time spent online as well as the speed and scale of the digital experience, the article aims to bring attention platform ecosystems in greater alignment with the interests of society without placing unrealistic expectations on individual users and parents…(More)”.