Stefaan Verhulst
Article by Hossein Bahadorizadeh & Mohammad Reza Malek: “Effective flood management in urban planning relies on accurate, timely data, which can be sourced from social media platforms for real-time post-flood damage information. However, many social media content lack location, creating a significant challenge for spatial analysis. This study addresses this gap by proposing a novel framework to infer the locations of post-flood events extracted from social media content, leveraging flood vulnerability maps and a structured knowledge base. The methodology involves four key steps, extracting flood-related events using hypergraph-based clustering; creating bounding boxes for potential event locations by integrating flood-related keywords, spatial proximity, and temporal patterns; constructing a knowledge base incorporating flood vulnerability criteria; and inferring event locations by comparing non-geo-tagged events against the knowledge base rules and aligning them with spatiotemporal bounding boxes. By analyzing 150,000 social media posts from flood-affected regions in southwestern Iran, such as Ahvaz, between April 6–16, 2019, the method identified 27 flood and 1200 post-flood events; of the 970 non-geo-tagged events, 69 were inferred inside the study region, while the remaining 901 were inferred out-of-region and were not mapped. Evaluation metrics, including 70% Precision, 77% Recall, and 74% F1 Score, show the model’s effectiveness in flood event detection, while spatial accuracy metrics, such as 2.15 km Mean Error Distance and 0.65 Mean Intersection over Union, confirm its reliability in location inference. The study highlights social media data’s potential for real-time flood management, especially in areas with scarce geotagged content…(More)”.
Article by Rekha Balu and William J. Congdon: “Federal economic data and statistics are essential for both public and private sector decisionmakers across the United States. They make it possible to monitor and understand the performance of the economy, craft public policy to effectively address challenges facing households and the nation, and make informed business and financial decisions. Their collective value to the users of these data—from policymakers to businesses to researchers—is immense.
Changing needs, and the need for changing data
At the same time, the needs of data users are evolving. Policymakers and businesses increasingly demand more timely, localized, and detailed information. Economic research continues to identify new relationships and concepts that are important for data to capture, and for statistical series to incorporate and reflect.
Most of all, economic data and statistics require constant innovation to keep pace with a dynamic and changing economy. Factors like the rise of artificial intelligence, gig work, digital assets, and increasingly complex sources of income and wealth can pose challenges for traditional economic data. Consider examples that arise across four key domains of economic data: employment, prices, income, and wealth:
Employment data: Understanding evolving labor markets
Federal employment statistics are among the most widely referenced economic indicators. These data—tracking labor market conditions, measuring job growth, calculating the unemployment rate, observing trends in and the distribution of wages across workers, and so on—are closely followed by policymakers, financial markets, researchers, voters, and the media…(More)“.
Article by Sara Schonhardt: “The United Nations is using artificial intelligence to more quickly identify emissions of a potent climate pollutant and alert governments and companies to act on them.
In the past two years, AI models have reviewed a host of new satellite data and flagged between 80 and 85 percent of methane releases for potential patching, according to a new report by the U.N. Environment Programme, which launched a Methane Alert and Response System in 2024.
That system has detected leaks that have released roughly 1.2 million metric tons of methane before being addressed — equivalent to the annual planet-warming pollution produced by 24 million cars. It’s one way of showing how AI can help mitigate the drivers of climate change, the report says.
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“As new satellite missions increase the volume of methane data available worldwide, the challenge is no longer finding emissions but acting on them,” Martin Krause, director of UNEP’s Climate Change Division, said in a statement. “AI can help bridge that gap, enabling faster identification of major methane releases and helping convert data into measurable emissions reductions.”..(More)”.
Report by Geoff Mulgan: “What can the academy actually tell us about building the institutions we need? Less than it should.
There is no shortage of brilliant work on how organisations behave. Economists trace how incentives shape institutional failure long before scandal does. Lawyers know that a vague mandate invites mission creep, while too narrow a one prevents adaptation. Anthropologists, following scholars like David Graeber, have shown that the official version of an institution and the version experienced by the person queuing inside it are often two different buildings entirely.
The trouble is that each discipline tends to describe the same animal without realising the others are touching it too. It is the old parable of the blind men and the elephant, retold across a dozen university departments: one insists it is a market, another a hierarchy, a third a culture, a fourth a constitutional order. Few step back to ask what the whole creature looks like, and fewer still ask how to build a better one…(More)”
Book edited by Akhil S.G., Latha Poonamallee, Simy Joy, Joanne Scillitoe, and Anita Howard: “Technological and scientific innovation does not simply emerge; it is designed. From organizational systems and data infrastructures to platforms, policies, and everyday tools, design choices shape how power operates, whose knowledge counts, and who benefits from innovation. Technology, Management, and Design for Social Justice brings together global scholars and practitioners to critically examine how design, management, and technological systems reproduce inequality, and how they can be intentionally reimagined to advance equity, dignity, and planetary wellbeing.
Moving beyond views of technology as neutral or inevitable, this volume positions design as a moral and political practice embedded in institutions and governance. Through conceptual frameworks and global case studies spanning algorithmic management, climate-oriented innovation, indigenous digital infrastructures, youth innovation ecosystems, and welfare technologies, the chapters show how justice is designed into (or out of) sociotechnical systems.
Written for scholars, advanced students, and practitioners across management, design studies, science and technology studies, and social justice, this book offers critical tools for rethinking how innovation is shaped, and for whom…(More)”.
Article by Yonghao Xu, Karen C. Seto & Qihao Weng: “The United Nations (UN) 2030 Agenda for Sustainable Development Goals (SDGs), specifically SDG 11, aims to create inclusive, safe, resilient, and sustainable cities. Over the past several decades, AI has contributed to the SDGs by improving planning, reducing congestion, and enhancing public services. However, it also introduces new systemic risks and governance complexities for cities. Compared to conventional AI systems, urban AI governance is particularly complex because the municipal government often acts as both deployers and regulators, with blurred lines of responsibility. Furthermore, urban AI is embedded in critical public infrastructure such as power grids and transportation systems, where failures could lead to serious societal, political, and economic consequences. Figure 1 outlines key applications, security threats, and policy roles in urban AI systems. It is foreseeable that AI security will become a global priority for sustainable urban development, yet current governance frameworks have not sufficiently addressed these challenges.
In this Comment, we conceptualize urban AI security as a socio-technical challenge encompassing two interrelated dimensions: algorithmic accountability and infrastructure security. The former concerns transparency, auditability, and mechanisms for accountability in AI-assisted public decision-making, while the latter involves the robustness and resilience of AI-embedded urban infrastructures against failures and attacks. We first examine the current governance landscape of urban AI and then analyze these two dimensions to identify key risks and policy gaps. Note that this Comment focuses on AI systems deployed in urban governance and infrastructure, rather than general or purely commercial AI applications…(More)”.
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)”