Stefaan Verhulst
A Curated Compilation of 100 Use Cases (2024-2026) by Stefaan Verhulst and Adam Zable: “The rapid digitization of society has fundamentally transformed the data landscape. Every day, billions of interactions with digital platforms, mobile devices, sensors, financial systems, satellites, connected infrastructure, and other technologies generate unprecedented volumes of information about human behavior, economic activity, environmental change, and public systems. While these data are typically created for operational, commercial, or technological purposes rather than official statistics or research, they increasingly offer valuable opportunities to address public-interest challenges when reused responsibly.
This so-called non-traditional data (NTD) has emerged as an important complement to conventional sources of evidence such as surveys, censuses, administrative records, and official statistics. It can provide information that is more timely, granular, continuous, and behaviorally rich than many traditional datasets, enabling governments, researchers, humanitarian organizations, and civil society to better understand rapidly changing conditions and respond more effectively.
From tracking disease outbreaks and population displacement to monitoring environmental degradation, estimating economic activity, improving disaster response, and informing urban planning, NTD is becoming an increasingly important part of the evidence base that supports public decision-making.
At the same time, the landscape for accessing and reusing non-traditional data is becoming more complex. Growing concerns around privacy, commercial sensitivity, cybersecurity, intellectual property, public trust, and the governance of artificial intelligence have led many organizations to restrict access to valuable datasets, contributing to what has been described as a “data winter”.
This has created a paradox: just as the potential public value of non-traditional data continues to expand, access to many privately held and platform-generated datasets is becoming more constrained. Unlocking that value therefore depends not only on technological innovation but also on effective governance, trusted stewardship, sustainable partnerships, and institutional arrangements that enable responsible data reuse.
Against this backdrop, the purpose of this report is to document how non-traditional data is already being reused in practice. Over the past two years, we have periodically identified and highlighted emerging examples of NTD reuse from around the world. This report brings together 100 curated use cases published between late 2024 and 2026 into a single resource. The compilation does not offer a comprehensive inventory of all existing applications, but seeks to provide a representative snapshot of the current state of practice across different sectors, geographies, and data types.
The cases illustrate the remarkable diversity of both the data being reused and the public-interest questions they seek to address. They span public health, humanitarian response, climate adaptation, environmental monitoring, disaster management, mobility, labor markets, economic measurement, agriculture, digital governance, education, and urban planning, among other domains. They also demonstrate how organizations are increasingly combining non-traditional data with traditional evidence sources, machine learning techniques, and domain expertise to produce more timely, actionable, and context-specific insights.
To provide a structured overview of this rapidly evolving field, the use cases are organized into seven broad data domains: (1) digital communication and online interaction data; (2) mobility and geolocation data; (3) health and biomedical data; (4) financial and commercial data; (5) work and labor market data; (6) in-home and Internet of Things (IoT) data; and (7) environmental, geospatial, and infrastructure data. Within each domain, examples are further grouped according to more specific data types. Each use case follows a common structure, describing the public-interest challenge being addressed, the role played by non-traditional data, and why the reuse of those data matters…(More)”
Article by Christoph Koettl: “For two decades, satellite imagery has been my window into the unreachable.
I’ve used it to expose North Korean oil smuggling and to uncover a mass grave in Burundi. In 2022, the Visual Investigations team at The New York Times used images to rebut Russian claims that the killing of civilians in Bucha, Ukraine, occurred after their soldiers had left. And in the U.S.-Israeli war in Iran, these eyes in the sky have been similarly revealing.
I surveyed the damage in Tehran from space shortly after Israeli strikes hit the compound of Iran’s supreme leader, Ayatollah Ali Khamenei, killing him. Our team tracked the damage that Iranian attacks wrought on regional U.S. bases. An image we captured through a satellite company even helped to determine U.S. responsibility for the strike on an elementary school in Minab, Iran, that killed at least 150 people, many of them children. And just last month, we showed how the United States bombed what appeared to be a drinking-water facility, a strike that if done deliberately could constitute a war crime under international law.
We reported some of these stories despite five U.S. satellite providers cutting off access to high-resolution images of Iran and surrounding countries shortly after the war began. The main reason for these restrictions is that Iran might use the imagery to target U.S. troops. This blackout applies to customers who regularly publish satellite imagery, such as news outlets and think tanks…(More)”.
Report by DemNext: “Democracy is under strain, and one of the most promising responses to that strain is the growing global movement around deliberative assemblies: citizens’ assemblies, citizens’ juries, and related forums that bring randomly selected, broadly representative groups of people together to weigh evidence, listen to one another, and make shared decisions on complex public issues. Over 1,000 such processes have now been run worldwide, and a growing body of evidence suggests they depolarise opinion, generate well-reasoned recommendations, build trust, and reconnect people to political life.
However, these processes are also resource-intensive, slow, and hard to scale, and have thus become a site of intense interest for AI integration. The pitch from many technologists, practitioners, and funders is consistent: AI can make deliberation cheaper, faster, more accessible, and more scalable.
In this paper, we argue that AI, when designed with care, can indeed play a powerful role in strengthening deliberation. But the very efficiencies that make AI attractive also risk undermining what deliberation is for in the first place. Whether AI strengthens or weakens deliberation or strengthens is not predetermined, however; it is a matter of design.
Our starting point is that deliberative assemblies are not decision-making machines whose sole value lies in the recommendation they produce. They are also spaces in which participants exercise and develop the civic capacities that democratic life depends upon. If we automate too much, we may end up with smoother processes that hollow out the productive friction that makes them valuable, while simultaneously reducing people’s ability to participate in democratic life.
These considerations are relevant to all places where deliberation takes place – workplaces, schools and universities, museums, financial institutions, corporations and cooperatives, membership-based associations, and other organisations.
We make three contributions.
First, we argue that one of the most important and most overlooked virtues of deliberative assemblies is that they build deliberative muscles: the cognitive, dispositional, and relational capacities that citizens need to do the work of democracy together. We use the language of muscle deliberately. A muscle is not an idea one holds; it is a capacity one maintains through practice, weakens when unused, and improves when trained.
Second, we offer a typology of seven deliberative muscles: self-reflection (examining one’s own values and beliefs), reasoning (engaging critically with evidence and expertise), dialogue (listening attentively, responding, and giving reasons), vulnerability (sharing feelings and reflections, tolerating conflict, feeling the weight of others’ experiences), collaboration (moving from individual reasoning to shared judgement), imagination (envisioning futures and alternatives concretely enough to deliberate about them), and facilitation (guiding small-group deliberation productively and inclusively)…(More)”.
Blog by WikiRate: “As artificial intelligence becomes increasingly commonplace, it is being presented as a solution for a broad swathe of tasks across different sectors. Company sustainability reporting is one example, which will be mandatory for companies operating in the EU with a turnover above €450 million and 1,000+ employees from 2027.
The European Sustainability Reporting Standards (ESRS) require companies to report digitally tagged data about their impact on the environment for the first time, helping data users understand companies’ exposure to risk and their effects on people and the planet.
However, a recent discussion paper from an organisation representing some of Germany’s largest companies argues that the digital tagging¹ of sustainability data is too costly and that AI should take over this task.
But could and should we use AI alone to digitally tag companies’ sustainability reports?
What is Digital-Tagging and why is it important for data users?
In the US and the EU, it is already standard practice for financial reporting data to be digitally tagged. The practice means hundreds of data points can be easily identified and quickly compared. Think of it like adding a footnote or reference to an essay. When a tag is added to a data point in a report, that point is added to a reference list, making it searchable, downloadable and comparable across companies.
Digital tagging will soon be extended to companies’ sustainability data reported under the ESRS. The sustainability standards will inform data users, including investors, governments, and the public, how well they understand and respond to risk. For example, green transition plans, supply chain grievance mechanisms, and workforce data demonstrate a company’s preparedness for environmental or human rights based risks. This information is crucial for enabling investors, policymakers, and consumers to make informed decisions regarding companies.
However, this development has not been universally welcomed, and some companies are arguing that “AI makes iXBRL [digital tagging] reporting increasingly obsolete. […] Given the significant costs and risks for issuers and the lack of added value for investors the only meaningful consequence would be to abolish ESEF/iXBRL reporting requirements entirely.”..(More)”
Article by Adrienne Martinez-Hollingsworth et al: “Artificial intelligence (AI) is the latest frontier of health care transformation, promising faster diagnoses, more accurate predictions, and new efficiencies. Yet for many of the nation’s community health centers (CHCs), this excitement carries an uncomfortable echo. Institutions asked to contribute data to power these tools have long memories of being studied, measured, and left behind; histories of extractive practices that consume data assets from public commons to sell products back at a premium. We, as a society, are woefully unprepared from a health policy and data-governance perspective for AI in health care. Without proactive guardrails in place, we risk rebranding yesterday’s extractive practices with today’s code…(More)”
Essay by Rose Horowitch: “Twenty-three hundred years ago, the legend goes, King Ptolemy I of Egypt asked his court adviser to assemble a comprehensive collection of the world’s written works. Ptolemy, who had served under Alexander the Great, envisioned a library that would safeguard the sum total of humanity’s knowledge. His successors inherited this mandate. Royal forces ransacked every ship that arrived at Alexandria, searching for scrolls. These were stored at the Mouseion, a shrine to the Muses modeled after Aristotle’s Lyceum. Aristotle’s own book collection was said to be among the holdings.
Much of the history of the Library of Alexandria has been lost. But we know that it was the site of many of the premodern world’s greatest intellectual achievements. The king paid scholars to live and work in the library, and the collection was available to anyone “eager to study, an encouragement for the entire city to gain wisdom,” a visiting Greek rhetorician wrote. It was at the library that Eratosthenes calculated Earth’s circumference and Zenodotus edited the earliest manuscripts of Homer’s epics. Euclid, who wrote the Elements of geometry, may have studied there as well.
This run of scholarship would not last. By 400 C.E., the library had disappeared. Many scholars regard its destruction as the greatest loss of knowledge in history and the beginning of the Dark Ages. Historians have spent centuries parsing fragments of papyrus in an effort to understand what went wrong.
Traditionally, the answer was believed to be war. During the Siege of Alexandria, in 48 B.C.E., Julius Caesar started a fire that incinerated at least 40,000 scrolls. The library survived in diminished form until the fourth century C.E., when followers of the archbishop of Alexandria sacked the pagan temple that housed the remaining manuscripts. But contemporary historians tend to dismiss the importance of these dramatic incidents in favor of a more mundane cause of death: negligence.
Maintaining the collection was an enormous expense. Humidity, mice, and insects slowly ate away at the papyrus scrolls. Scribes had to continually copy old texts before they deteriorated and became illegible. Eventually, the challenges of maintaining the library became greater than the will to preserve it. “It is not that the disappearance of a library led to a dark age, nor that its survival would have improved those ages,” the classics scholar Roger Bagnall has written. The fact that the library was allowed to die showed that the dark age had already arrived.
Some 2,000 years later, under very different circumstances, the darkness is gathering again. Americans, once members of a proudly literate society, read much less than they used to. According to the National Endowment for the Arts, which conducts the most comprehensive survey of the nation’s reading habits, fewer than half of all adults reported having read a book of any kind in 2022. Only 38 percent read a novel or short story. A study analyzing 236,000 responses to the American Time Use Survey found that the proportion of Americans who read for pleasure on any given day fell from 28 percent in 2004 to 16 percent in 2023. (The study looked at people who had read a book, magazine, or newspaper; listened to an audiobook; or read an e-book.) Gambling has become a more common leisure activity than reading a book: Last year, 57 percent of Americans placed a bet…(More)”.
Book edited by Sheila L. Macrine, Jennifer M. B. Fugate, Arsen Abdulali and Josie Hughes: “Intelligence research is undergoing a radical transformation, moving beyond the traditional focus on brains and code to increasingly recognize the role of embodiment, as well as our understanding of goal-directed behavior across different scales and substrates. In this edited collection, experts across fields including philosophy, phenomenology, neuroscience, cognitive psychology, robotics, AI, bio-inspired design, biology, and bioengineering initiate transdisciplinary dialogues and facilitate the sharing of insights on embodiment, enabling a new understanding of embodied intelligence and how intelligence manifests across diverse substrates.
By embracing a broad definition of embodied intelligence, the contributors transcend the traditional divide between the biological and the artificial, recognizing the potential for intelligence to emerge in unexpected forms. This perspective challenges us to reconsider our assumptions about the nature of intelligence and to appreciate the remarkable diversity of intelligent behavior in the world around us. This broadened concept holds immense promise to profoundly reshape our understanding of ourselves, the technologies we create, and the very nature of intelligence itself…(More)”.
Tool developed by the Center for Collective Learning (CCL): “Explore academic impact – without flattening it into a rank.
Rankless visualizes the global flow of ideas across universities, journals, scholars, and countries – revealing who influences whom, where knowledge travels, and which topics bind the world together…
We need to understand more and rank less.
Rankings flatten complexity into a single number. Rankless restores the context: domain, geography, collaboration, and time. Use it to make better-informed decisions – whether you’re a student, researcher, policymaker, or funder.
Rankless is an experimental data visualization project that allows users to explore the impact of thousands of universities. It is built on the idea that universities generate impact that is specific to a geography and to certain topics, and that rankings obscure that impact by reducing it to a single dimension. By transcending rankings, we highlight a university’s multidimensional impact by showing you who they work with and who cites them. To understand more, sometimes, we need to rank less…(More)”.
Essay by Akash Wadhwani: “On the morning of February 6, 2023, an earthquake killed more than fifty thousand people in Türkiye and Syria. I spent a week inside the edit history of the world’s free map, reading what the internet did that morning. I haven’t stopped thinking about it…
Why would a city of half a million people be missing from the map? That sounded impossible to me in 2023, so I went and looked it up, and the answer turns out to be money, twice.
Commercial maps are built where the money is. Google and Apple map roads because cars navigate them, and they map shops because businesses pay to be found. That works beautifully in London or Los Angeles. In a working-class Turkish city, and in the Syrian towns across the border, there is no ad revenue in knowing where each house stands, so no company ever paid to find out. The satellites photograph everything, but a photograph is not a map. Someone still has to look at the pixels and say: this shape is a building, this line is the road that reaches it.
The second reason surprised me more. Even where a commercial map looks complete, rescue teams mostly cannot use it. They can’t download it onto a GPS unit and carry it into a zone where the internet is down. They can’t count its buildings to estimate how many people might be trapped in a district. The data belongs to the company, and the license says no. OpenStreetMap is the exception, and it is the exception on purpose: it is the Wikipedia of maps, free for anyone to copy, carry, and analyse. When things go wrong, it is the map that gets used. It just has to be drawn first, by someone.
That morning, rescue teams were already in the air. They were flying toward a city the free map could not yet describe.
You cannot search rubble you don’t know exists…(More)”.
Essay anthology by the Bennett School of Public Policy : “…brings together visionary thinkers, policymakers, and experts to challenge the narrative of AI as a zero-sum competition.
The idea that AI is an arms race between the US and China has taken hold of contemporary geopolitics. But is it the right framing?
Drawing on diverse perspectives from diplomacy, philanthropy, civil rights, national security and economics, the essays in this anthology explore the limitations of the arms race metaphor and ask key questions about its origins and influence. While each author offers a unique viewpoint from their own expertise, taken together their collective insights reveal the shortcomings of this framework as a lens for interpreting the complex geopolitical realities of AI and reveal alternative ways of understanding AI’s geopolitical influence and potential…(More)”.