Stefaan Verhulst

Article by The National Archives: “Re-using public sector information is a government priority, one the UK Industrial Strategy estimates could unlock billions in economic growth. Yet publishing data openly, and enabling its re-use, is a process full of friction for public institutions. As the authority on licensing, The National Archives and their digital and policy teams partnered with IF to understand why the licensing framework is challenging to use today, prototype a redesign, and recommend how The National Archives can take a leadership role in closing the gaps so that people can use it with confidence…(More)”.

Article by Sarah O’Connor and John Burn-Murdoch: “In the UK, the beleaguered Labour government has high hopes that AI can help to deliver better, faster and cheaper public services. Just this week, the FT’s public policy editor Chris Smyth reported on a draft proposal to scale back recruitment plans for the NHS, which is partly premised on the expectation that much wider use of AI can boost productivity and even “completely substitute” for some (unspecified) roles.

But how easy is it to insert AI into public services? And how will we know if it’s working?…

Here in the UK, adoption of AI tools is gathering pace across many parts of the public sector. As far back as early 2024, the National Audit Office found that almost three-quarters of government departments and national bodies were either deploying or piloting AI tools. Some of these deployments are very narrow. The Department for Transport, for example, is trialling an AI tool to prevent fraud when people apply for subsidies for electric vehicle chargers, by checking “proof of installation” photos against similar or stock photos. Others are more widespread and seem to have developed quite organically, such as the rapid adoption of AI transcription tools by social workers to record meetings and save time typing up notes.

But how do we know if any of this is making a difference when it comes to productivity?..(More)”.

Paper by Joseph Low, Oscar Duys, Claude Formanek, Lewis Hammond, and Michiel Bakker: “Deliberative democracy arguably leads to better collective decisions, but is fundamentally constrained by human attention and bandwidth. While recent AI-mediated deliberations scale participation by synthesizing inputs from many humans, they remain time-intensive for individual users. As AI models become increasingly capable, AI systems are being deployed not only to mediate deliberation between humans, but to represent humans in it: where AI agents deliberate on behalf of human users. We call this paradigm AI-delegated deliberation. While it promises unprecedented scale for democratic participation, it introduces qualitatively new design and alignment challenges that are poorly understood and under-theorized. To study these dynamics empirically, we deploy Habermolt, a public platform for AI-delegated deliberation. We evaluate its effectiveness along three dimensions that we use to organize any deliberative system: representation, aggregation, and revision. We use these observations to illuminate the design decisions future AI-delegated deliberation platforms must confront, contributing to the broader research agenda for scalable yet trustworthy AI representatives…(More)”.

Report by the OECD: “Across major economies, trustworthy artificial intelligence is rapidly moving from high-level policy to deployment in core industries such as health, manufacturing and mobility. The European Union is positioning itself for this shift by focusing not only on innovation capacity but also on trustworthy and coordinated implementation across its Member States. Gaining a deeper understanding of where AI is already being applied and gathering evidence on determinants of adoption are essential to assess Europe’s competitiveness and policy readiness.

The European Union is pursuing its ambition to become a global leader in trustworthy AI, moving from high-level policy to on-the-ground implementation. The OECD worked closely with the European AI Office to monitor efforts to develop trustworthy AI and promote its development across the European economy, with a two-volume publication series analysing how this transition is taking place in practice. The first volume focuses primarily on national strategies, initiatives and governance mechanisms for AI in EU Member States. The second, Progress in Implementing the European Union Coordinated Plan on Artificial Intelligence (Volume 2), shifts the lens to sector-specific impact…(More)”.

Article by Cyrus Moulton: “It’s a common question among locals whenever there’s a particularly large crowd at a special event or a local tourist attraction: “Where are these people coming from?”

A new online tool from the Social Urban Networks lab (SUNLab) with the Network Science Institute and the Boston Area Research Initiative at Northeastern University may be able to help. 

“Communities are not only defined by who lives there,” the teams said in a prepared statement. “They are defined by who shows up, where they come from, and what they do.”

Called Mobility Data for Communities, or MD4C, the tool uses cellphone location data — aggregated and anonymized to protect privacy — to help communities better understand their visitors. The platform reveals such insights as who is coming to different communities, where these visitors come from, can estimate what activities they do in a community, how long they spend there, and more.

“The idea is to create what we call a ‘dynamical census’ in which we show who are the people visiting an area, where they come from, what they do when they are in that area, and how much time they spend, etc.,” said Esteban Moro, a professor of physics and director of SUNLab…(More)“.

Article by Chen Sun, Nikolas Guggenberger, and Supreeth Shastri: “Ancient wisdom says that everything that has a beginning has an ending. However, when it comes to the lifecycle of personal data, the ending is nowhere in sight. In fact, for much of its existence, the computing community has evolved without treating deletion as a first-class operation. This practice had to change when the European Union introduced Right to be Forgotten (RTBF)—first in 2014, as a standalone right applicable to online search engines and then in 2018, as a universal right applicable to all data controllers through the General Data Protection Regulation (GDPR).

“Isn’t it just deletion?” has been the computing community’s standard reaction to the RTBF’s requirements. While the end goal of RTBF is indeed the deletion of data, casting RTBF as just deletion is akin to saying that eating is just for nutrition. It is not surprising that over the first five years of GDPR, an RTBF penalty is issued once every nine days—a clear sign that the computing and data management communities have continued to oversimplify, misunderstand, and poorly implement RTBF. Our work is an attempt to remedy this disconnect.

This article demonstrates how RTBF exposes computing systems to uncertainties and the challenges at all stages of design and operation, and how RTBF has invalidated principles and practices of data management with decades of precedent. To address these challenges, we propose a principled approach for introducing RTBF capability in computing systems. Our solution is rooted in two key insights:

1. The need to bridge an intrinsic dichotomy existing between computing and law, that is, computing systems are created to be precise and static, but laws are written to be abstract and interpretable
2. Modeling compliance as a via negativa problem, that is, instead of trying to build a perfectly compliant system, it is much easier to weed out known violations from the system…(More)”.

Paper by Susan Ariel Aaronson and Michael Moreno: “AI governance needs to be democratic. Canada has stated that it wants to hear from a wide range of Canadians for its consultation process on AI governance, offering constituents a few options to provide insights, but there is much more Canada (and other democracies) could do to meaningfully inform, involve and collaborate with their citizens on AI governance.

Through comparing Canada’s approach to AI governance consultations in Australia, Colombia and the United States, Susan Ariel Aaronson and Michael Moreno provide the following recommendations to improve democracies’ AI governance practices to better involve their constituents:

• Build a base of common knowledge among citizens about AI to support informed participation.
• Recognize public participation in AI governance as both a policy problem and a marketing problem. Enlist the help of a wide range of civil society groups on outreach.
• Establish an “always-on” portal where citizens can ask questions and provide feedback about AI policies.
• Designate an ombudsperson at every government department to respond to public concerns over policies and practice related to AI. The ombudsperson should investigate citizen complaints and resolve them…(More)”.

Report by the World Economic Forum: “Digital transformation offers governments a powerful pathway to improve public service delivery and strengthen state capability. Yet without clear guardrails – and amid incentives that often prioritize speed and visible delivery – these efforts can deepen exclusion, weaken accountability and produce systems that fail to reflect the lived realities of citizens, ultimately eroding public trust and government legitimacy.

In practice, these challenges stem less from the technology itself than from how decisions are made across the design and deployment of GovTech and digital public infrastructure (DPI) solutions. The GovTech Compass: Ten Principles for the Responsible Implementation of GovTech and Digital Public Infrastructure responds to this gap by offering a principles-based framework for more responsible and effective digital transformation. It sets out 10 practical principles to guide decision-making across the GovTech and DPI life cycle, helping governments and ecosystem stakeholders navigate trade-offs, align incentives and embed public value at the core of implementation…(More)”.

Paper by Mark P. Khurana, et al: “Human mobility, climate change and demographic trends increase the risk of pathogen spillover and expansion. Data that can inform our responses to outbreaks have increased in availability and volume, but access to highly confidential outbreak data and commercially sensitive contextual information remains difficult. Despite ongoing efforts to adopt global health data infrastructures and sharing protocols, there remain regulatory, logistical, human and computational barriers to data sharing. Federated approaches—in which data remain stored locally but analyses are performed across datasets from different sources—offer a potential way to address these challenges. While federated approaches have been used in some clinical and biomedical contexts, their adoption in infectious disease surveillance and modeling has been limited. Here, we discuss global approaches to infectious disease modeling and analysis, with a focus on federated methods. We outline how these can be used to address key epidemiological questions during outbreaks by enabling the secure use of multimodal data and integration with existing surveillance and modeling efforts. We summarize current methods for combining distributed and locally stored data and identify limitations, opportunities and organizational structures needed to achieve equitable global public health impacts…(More)”.

Report by Stefaan Verhulst: “Europe has built data initiatives such as the European Open Science Cloud and the European Health Data Space, but these mainly focus on traditional sector-specific data. This paper argues that non-traditional data from platforms, sensors, mobility systems, and consumer behaviour could significantly improve health and well-being research. Evidence from more than 290 studies shows these datasets can help detect health risks earlier, identify inequalities, and connect environmental exposure to disease outcomes. To unlock this potential, Europe must overcome barriers including fragmented access, weak public trust, and short-term funding. The paper proposes six policy actions to create effective health data ecosystems…(More)”.