Stefaan Verhulst

Book by Chris Haufe: “There is in certain circles a widely held belief that the only proper kind of knowledge is scientific knowledge. This belief often runs parallel to the notion that legitimate knowledge is obtained when a scientist follows a rigorous investigative procedure called the ‘scientific method’. Chris Haufe challenges this idea. He shows that what we know about the so-called scientific method rests fundamentally on the use of finely tuned human judgments directed toward certain questions about the natural world. He suggests that this dependence on judgment in fact reveals deep affinities between scientific knowledge and another, equally important, sort of comprehension: that of humanistic creative endeavour. His wide-ranging and stimulating new book uncovers the unexpected unity underlying all our efforts – whether scientific or arts-based – to understand human experience. In so doing, it makes a vital contribution to broader conversation about the value of the humanities in an increasingly STEM-saturated educational culture…(More)”.

Report by Akash Kapur, and Gillian Rosenberg: “The AI conversation is dominated by a preoccupation with frontier capabilities. Yet a more consequential challenge is the gap between what AI can do and what it is actually doing in the world. This adoption gap is particularly acute in the Global South, where AI is deployed amid resource constraints, limited state capacity, and fragile institutions. This report sets out to map these adoption challenges, in order to understand how AI can be better embedded into existing workflows and social structures. Drawing on interviews, surveys, and case studies, it builds this case through granular, ground-up evidence, pushing back against the abstraction that so often characterizes the field.

Key Findings

• AI adoption is fundamentally a problem of alignment with real-world systems, not model capability; contextual fit matters more than benchmark performance.
• Adoption is a whole-of-society challenge that cannot be resolved at the technical layer alone.
• Constraint is generative as well as limiting: Resource scarcity is producing distinctive forms of innovation—frugal engineering, modular compute, edge deployment, participatory data collection—that may have relevance well beyond the Global South.
• Adoption gaps are unevenly distributed and tend to compound for already excluded populations, particularly women…(More)”.

Paper by Santiago Andrés Azcoitia: “…we examine whether the Data Governance Act (DGA) regime on data intermediation services is adequate to achieve its intended purpose of fostering trust, neutrality, and sustainability in data sharing. We review the state of the art in data intermediation technologies in both industry and academia and discuss its interplay with the regulation. We identify friction points with existing data markets and anticipate challenges in emerging contexts, such as intermediaries delivering machine learning models or using confidential computing techniques. Finally, we explore potential amendments to, or interpretations of, the DGA required to promote a data intermediation model that is both economically viable and neutral…(More)”.

Paper by Michael B. Hawes et al: National Statistical Organizations (NSOs) and other groups often use the term “for statistical purposes only” in communication with prospective respondents, data users and other stakeholders. This term also provides an important anchor for many NSO decisions on operations and ethics. Although public communication often omits a clear operational definition of this term, this paper will show that reviews of underlying laws and policies identify two predominant criteria: (1) production of statistical information about relatively large population aggregates, with the intention of creating a general public benefit; and (2) protection of the confidentiality of data collected about data subjects and a related prohibition against the use of information provided by or about data subjects for legal or regulatory action against those individuals or organizations. We then explore how this term exists, and is often interpreted, within a much broader landscape of legal requirements and of scientific and professional codes of practice, and provide a broader definition for consideration based on these and other ethical frameworks. This paper closes by highlighting several areas that warrant further discussion to better position NSOs to navigate these challenges in the future…(More)”

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)”.