Stefaan Verhulst

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)”.

Article by Tom Fleischman: “It was born in northern New Mexico, the brainchild of then-Los Alamos National Laboratory physicist Paul Ginsparg, Ph.D. ’81, as a simple way for researchers to share their work with colleagues before it appeared in peer-reviewed journals.

When Ginsparg returned to Cornell as a professor in 2001, he brought the online research repository arXiv with him. And for 25 years it has remained at Cornell, where it has grown into a global clearinghouse for millions of research papers, accessible to anyone with an internet connection.

Now, arXiv embarks on its next chapter: a transition to an independent nonprofit. The move will enable faster technological development, greater organizational flexibility, expanded partnerships and long-term financial sustainability.

“This is something we’ve talked about for a long time,” said Greg Morrisett, the Jack and Rilla Neafsey Dean of Cornell Tech, where arXiv is headquartered. “To make sure for the long run that it was going to be supported, well beyond a particular dean valuing it, we felt like it was a responsible thing to do.”

The move will become official July 1; arXiv headquarters will remain in Cornell Tech’s Tata Innovation Center. The search is on for an inaugural CEO as well as a board of directors…(More)”.

Article by Stefaan Verhulst: “Official statistics have long served as the bedrock of evidence-based policymaking in the United States, but the ground beneath them is shifting. Survey response rates are falling; collection costs are rising; privacy concerns are mounting; and public trust in government information has eroded just as the questions policymakers must answer—about digital inclusion, financial resilience, climate impacts, mobility, and economic opportunity—have grown more complex and time-sensitive. The statistical systems built for a slower, more uniform economy were never designed to keep pace with a society this dynamic.

Out of this tension has come a quiet but consequential shift: the rise of the re-use of non-traditional data, or NTD. Generated continuously through commercial transactions, digital platforms, connected devices, satellites, financial institutions, mobility services, and online interactions, this data was never collected with statistical production in mind. Yet when it is responsibly governed and woven together with surveys, censuses, and administrative records, it gives statistical agencies something they have always wanted but rarely had: a near-continuous, granular window into economic and social life. The promise is not that non-traditional data will replace official statistics but that it will make them faster, cheaper, and more relevant—while asking less of the public in the process.

As in other countries, the United States has become a testing ground for this hybrid approach. Federal statistical agencies — the Census Bureau, the Bureau of Labor Statistics, and the Bureau of Economic Analysis among them — have increasingly partnered with universities, nonprofits, philanthropies, and private companies to explore what non-traditional data can offer. Outside government, organizations such as Opportunity Insights, the JP MorganChase Institute, Microsoft Research, Mastercard’s Center for Inclusive Growth, and Meta’s Data for Good program have shown that privately held, passively generated data can produce policy-relevant indicators that official statistics alone cannot.

What emerges from these efforts is not competition between data sources but complementarity. Surveys still offer representativeness (“ground truth”) and rich context; administrative records still offer comprehensive population coverage; and non-traditional data contributes something that is often limited in both: timeliness, granularity, and the ability to see change as it happens rather than months later.

The examples that follow provide a snapshot of current experimentation in the United States. They are not intended to be exhaustive, but rather to illustrate the diversity of approaches through which non-traditional data is being integrated with surveys, censuses, and administrative records to strengthen official statistics and inform public decision-making…(More)”.

Book by Sherry Turkle: “If social media came for our attention, artificial intelligence is now coming for our capacity for attachment. Chatbots that speak to us in a human voice offer themselves as best friends, lovers, and psychotherapists. As of 2025, over 70% of teens and nearly one-third of US adults rely on AI for companionship and emotional support, with many preferring these chatbot relationships over human ones. 

When we talk to chatbots in these roles, as intimate machines, we accept as sufficient what machines can offer: the mere performance of intimacy, empathy, and love. We begin to think that pretend empathy is empathy enough. We redefine human capacities for care, solitude, and intimacy in terms of what machines can do. Sherry Turkle, the psychologist who pioneered our understanding of human-computer relationships, calls the new culture of chatbots artificial intimacy, our new AI. 

Through compelling storytelling, framed by Turkle’s decades of experience as a chronicler and analyst of digital culture, Artificial Intimacy evokes the seductive and beguiling nature of chatbots. They can organize our calendars, plan our travel, or analyze our stock picks, all with an efficiency that outstrips what a person might do. 

And then, they promise to be more—to be our “perfect” companion. They will always be there for us, listen to us, and support us—and ask for nothing in return. But these intimate machines, warns Turkle, are producing a generation more alienated, depressed, and lonely than ever before. More than that, we become less equipped to reverse course—machine relationships do not offer practice for getting along with people. 

Artificial Intimacy is unique in how it traces our new habit of talking to machines through the lifecycle—from children’s earliest attachments to how we face death. But technology, by offering to do everything, teaches us that we neither need nor have the capacity to take risks, have hard conversations, struggle through uncertainty or insecurity, or rely on our own faculties and judgment. 

Turkle has spent decades studying how digital technologies isolate us from one another. Now, in her long-awaited follow-up to Reclaiming Conversation, she offers both a cautionary tale and a roadmap for reclaiming our humanity in the age of AI…(More)”.