Stefaan Verhulst

Article by Adam Zable, Stefaan Verhulst: “Non-Traditional Data (NTD) — data digitally captured, mediated, or observed through instruments such as satellites, social media, mobility apps, and wastewater testing — holds immense potential when re-used responsibly for purposes beyond those for which it was originally collected. If combined with traditional sources and guided by strong governance, NTD can generate entirely new forms of public value — what we call the Third Wave of Open Data.

Yet, there is often little awareness of how these datasets are currently being applied, and even less visibility on the lessons learned. That is why we curate and monitor, on a quarterly basis, emerging developments that provide better insight into the value and risks of NTD.

In previous updates, we focused on how NTD has been applied across domains like financial inclusion, public health, socioeconomic analysis, urban mobility, governance, labor dynamics, and digital behavior, helping to surface hidden inequities, improve decision-making, and build more responsive systems. 

In this update, we have curated recent advances where researchers and practitioners are using NTD to close monitoring gaps in climate resilience, track migration flows more effectively, support health surveillance, and strengthen urban planning. Their work demonstrates how satellite imagery can provide missing data, how crowdsourced information can enhance equity and resilience, and how AI can extract insights from underused streams.

Below we highlight recent cases, organized by public purpose and type of data. We conclude with reflections on the broader patterns and governance lessons emerging across these cases. Taken together, they illustrate both the expanding potential of NTD applications and the collaborative frameworks required to translate data innovation into real-world impact.

Categories

• Public Health & Disease Surveillance
• Environment, Climate & Biodiversity
• Urban Systems, Mobility & Planning
• Migration
• Food Security & Markets
• Information Flows for Risk and Policy..(More)”.

Article by Gideon Lichfield: “Point your browser at publicai.co and you will experience a new kind of artificial intelligence, called Apertus. Superficially, it looks and behaves much like any other generative AI chatbot: a simple webpage with a prompt bar, a blank canvas for your curiosity. But it is also a vision of a possible future.

With generative AI largely in the hands of a few powerful companies, some national governments are attempting to create sovereign versions of the technology that they can control. This is taking various forms. Some build data centres or provide AI infrastructure to academic researchers, like the US’s National AI Research Resource or a proposed “Cern for AI” in Europe. Others offer locally tailored AI models: Saudi-backed Humain has launched a chatbot trained to function in Arabic and respect Middle Eastern cultural norms.

Apertus was built by the Swiss government and two public universities. Like Humain’s chatbot, it is tailored to local languages and cultural references; it should be able to distinguish between regional dialects of Swiss-German, for example. But unlike Humain, Apertus (“open” in Latin) is a rare example of fully fledged “public AI”: not only built and controlled by the public sector but open-source and free to use. It was trained on publicly available data, not copyrighted material. Data sources and underlying code are all public, too.

Although it is notionally limited to Swiss users, there is, at least temporarily, an international portal — the publicai.co site — that was built with support from various government and corporate donors. This also lets you try out a public AI model created by the Singaporean government. Set it to Singaporean English and ask for “the best curry noodles in the city”, and it will reply: “Wah lau eh, best curry noodles issit? Depends lah, you prefer the rich, lemak kind or the more dry, spicy version?”

Apertus is not intended to compete with ChatGPT and its ilk, says Joshua Tan, an American computer scientist who led the creation of publicai.co. It is comparatively tiny in terms of raw power: its largest model has 70bn parameters (a measure of an AI model’s complexity) versus GPT-4’s 1.8tn. And it does not yet have reasoning capabilities. But Tan hopes it will serve as a proof of concept that governments can build high-quality public AI with fairly limited resources. Ultimately, he argues, it shows that AI “can be a form of public infrastructure like highways, water, or electricity”. 

This is a big claim. Public infrastructure usually means expensive investments that market forces alone would not deliver. In the case of AI, market forces might appear to be doing just fine. And it is hard to imagine governments summoning up the money and talent needed to compete with the commercial AI industry. Why not regulate it like a utility instead of trying to build alternatives?..(More)”

Report by Darya Minovi: “The Trump administration is systematically attacking a wide range of public health, environmental, and safety rules. By law, federal agencies must notify the public about potential rule changes and give them the opportunity to make comments on those changes. But in many cases, the Trump administration is evading that legal requirement.

In the first six months in office, roughly 600 final rules were issued across six key science agencies. In 182 of these rules, the administration bypassed the public notice and comment period, cutting the public out of the process of shaping rules that affect their health and safety and our planet. This undermines the principles of accountability and transparency that should be part of our democracy…(More)”.

Editorial by Christian Fynbo Christiansen, Persephone Doupi, Nienke Schutte, and Damir Ivanković: “The European Health Data Space (EHDS) regulation creates a health-specific ecosystem for both primary and secondary use of health data. HealthData@EU—the novel cross-border technical infrastructure for secondary use of electronic health data will be crucial for achieving the ambitious goals of the EHDS.

In 2022, the “HealthData@EU pilot project,” co-funded under the EU4Health-framework (GA nr 101079839), brought together 17 partners including potential Health Data Access Bodies’ (HDABs) candidates, health data sharing infrastructures, and European agencies in order to build and test a pilot version of the HealthData@EU infrastructure and provide recommendations for metadata standards, data quality, data security, and data transfer to support development of the EHDS cross-border infrastructure.

This editorial and the other manuscripts presented in this Special EJPH Supplement will provide readers with insights from real-life scenarios that follow the research user journey and highlight the challenges of health research, as well as the solutions the EHDS can provide…(More)”.

Paper by Barbara J Evans and Azra Bihorac: “As nations design regulatory frameworks for medical AI, research and pilot projects are urgently needed to harness AI as a tool to enhance today’s regulatory and ethical oversight processes. Under pressure to regulate AI, policy makers may think it expedient to repurpose existing regulatory institutions to tackle the novel challenges AI presents. However, the profusion of new AI applications in biomedicine — combined with the scope, scale, complexity, and pace of innovation — threatens to overwhelm human regulators, diminishing public trust and inviting backlash. This article explores the challenge of protecting privacy while ensuring access to large, inclusive data resources to fuel safe, effective, and equitable medical AI. Informed consent for data use, as conceived in the 1970s, seems dead, and it cannot ensure strong privacy protection in today’s large-scale data environments. Informed consent has an ongoing role but must evolve to nurture privacy, equity, and trust. It is crucial to develop and test alternative solutions, including those using AI itself, to help human regulators oversee safe, ethical use of biomedical AI and give people a voice in co-creating privacy standards that might make them comfortable contributing their data. Biomedical AI demands AI-powered oversight processes that let ethicists and regulators hear directly and at scale from the public they are trying to protect. Nations are not yet investing in AI tools to enhance human oversight of AI. Without such investments, there is a rush toward a future in which AI assists everyone except regulators and bioethicists, leaving them behind…(More)”.

Paper by Yaniv Benhamou & Mélanie Dulong de Rosnay: “The present contribution proposes a novel commons-based copyright licensing model that provides individuals better control over all their data (including copyrightable, personal and technical data) and that covers recent developments in AI technology. The licensing model also proposes restrictions and boundaries (e.g. to authorised users and groups) to protect the commons, allowing communities to define and maintain the political values they choose. Building on the practice of collective management of copyright, it also empowers data trusts to govern and monitor the use and re-use of the concerned data. The model is ultimately meant to address the power imbalance and information asymmetry that characterise today’s economy of data as well as the “data winter” effect that restricts the accessibility of data for public interest, while accommodating and empowering individuals and communities that may have different political values and visions…(More)”.

OECD Report: “The growing demand for high-quality data to inform policy and to enable trustworthy artificial intelligence has increased the relevance of trusted data intermediaries (TDIs). National statistical offices (NSOs) are uniquely positioned to serve as TDIs, given their mandates and public trust. This paper examines practices across 16 NSOs and finds that many are expanding beyond their traditional remit to facilitate data sharing among public administrations, researchers and, in some cases, private actors. These institutions employ robust confidentiality and privacy safeguards, adopt privacy enhancing technologies (PETs) and operate secure research environments. Oversight mechanisms, trust building and adequate resources are essential for NSOs to succeed in this evolving role. The analysis highlights the importance of NSOs as emerging actors within data ecosystems to support both evidence-based policymaking and the responsible development of AI. It also underscores the need for additional resources and support to ensure NSOs can undertake these expanding roles…(More)”.

Article by Kate Hodkinson: “Novel data sources can provide important proxies in data-limited contexts. When combined with traditional humanitarian data, such as needs assessments or displacement tracking, these sources can increase the resilience of the humanitarian data ecosystem. For example, in response to the March 2025 Myanmar earthquake, Microsoft AI for Good Lab provided data on building damage before access to affected areas was possible. In other cases, Meta’s high-resolution population density maps have been used to estimate the number of people living within a 30-metre grid and their demographics, helping organisations identify people in need.

Applications of this novel data sources have been explored through many pilots over the last decade. However, progress has not been linear. Seemingly promising technologies have fallen into obscurity, while others have carved out clear use cases. How should humanitarians use novel data sources to reinforce informational resilience, rather than create new dependencies?

Exploring this question required a structured rubric through which we could assess the integration of novel data sources to date, understand their technosocial context, and consider the factors that may define their future use in the humanitarian sector. We used two tools to do this: the S-Curve and the Technology Axis Model.

The S-Curve

The humanitarian sector’s adoption of novel data sources can be mapped against an S-Curve (adapted from Fisher, 1971), which measures maturity from nascent potential to normative practice. For example, the use of satellite data to create dynamic, high-resolution population estimates has moved from a ‘nascent’ opportunity towards a common practice, proving particularly valuable in contexts with limited or outdated census data. The S-Curve creates a way to plot examples of where novel data sources have been integrated into crisis response analysis or appear trapped in a perpetual pilot phase.

The Technology Axis Model (TAM) positions technology innovation cycles as happening in four inter-linked areas:

1. The development of the underlying technology.
2. The social norms and values associated with the innovation and its effects.
3. The applications that are emerging as entrepreneurs seek to introduce the technology into markets.
4. The infrastructure and systems that govern the technology area.

The Technology Axis Model was originally developed by Bill Sharpe as a way to help engineers think more widely about the contexts that surround technology innovation. Our definitions are drawn from a forthcoming paper on the Technology Axis Model, by Bill Sharpe and Andrew Curry…(More)”.

OECD Report: “…explores the concept of openness in artificial intelligence (AI), including relevant terminology and how different degrees of openness can exist. It explains why the term “open source” – a term rooted in software – does not fully capture the complexities specific to AI. This paper analyses current trends in open-weight foundation models using experimental data, illustrating both their potential benefits and associated risks. It incorporates the concept of marginality to further inform this discussion. By presenting information clearly and concisely, the paper seeks to support policy discussions on how to balance the openness of generative AI foundation models with responsible governance…(More)”

Book by Cass R. Sunstein: “New technologies are offering companies, politicians, and others unprecedented opportunity to manipulate us. Sometimes we are given the illusion of power – of freedom – through choice, yet the game is rigged, pushing us in specific directions that lead to less wealth, worse health, and weaker democracy. In, Manipulation, nudge theory pioneer and New York Times bestselling author, Cass Sunstein, offers a new definition of manipulation for the digital age, explains why it is wrong; and shows what we can do about it. He reveals how manipulation compromises freedom and personal agency, while threatening to reduce our well-being; he explains the difference between manipulation and unobjectionable forms of influence, including ‘nudges’; and he lifts the lid on online manipulation and manipulation by artificial intelligence, algorithms, and generative AI, as well as threats posed by deepfakes, social media, and ‘dark patterns,’ which can trick people into giving up time and money. Drawing on decades of groundbreaking research in behavioral science, this landmark book outlines steps we can take to counteract manipulation in our daily lives and offers guidance to protect consumers, investors, and workers…(More)”