Stefaan Verhulst

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)”.

Paper by Stefaan Verhulst, Roshni Singh, Marta Dell’Aquila, Leonie Kunze, and Cosima Lenz: “Women’s health research remains under-resourced, underprioritized, and narrowly defined. Across the life course, women experience distinct health needs with significant implications for health and wellbeing, yet persistent gaps in evidence and data continue to reinforce inequities. In the absence of a universally accepted definition of women’s health, this study aimed to develop a topic map to capture its breadth and to identify an evidence-informed set of the top ten priority questions to guide future women’s health research and innovation.
Methods: We used a participatory, iterative methodology inspired by the 100 Questions Initiative, combining structured stakeholder engagement, rapid evidence synthesis, and iterative validation. An initial topic map was developed through an in-person workshop and refined through ongoing engagement with 77 global experts in women’s health and data science. Guided by the topic map, experts submitted research questions via a virtual survey, which were refined, clustered, prioritized, and ranked.
Results: The topic map served as a shared framework to guide the submission of actionable research questions and comprised four branches: (1) key domains of women’s health; (2) determinants and barriers; (3) technology and innovation; and (4) research and evidence gaps. A total of 113 questions were submitted, clustered into 56 themes, and narrowed to a top ten through expert prioritization, followed by public ranking via a virtual survey that yielded 115 responses. The highest-ranked questions focused on reframing and prioritizing women’s health, strengthening investment and innovation ecosystems, and addressing evidence gaps, research participation, data quality, and equity.
Conclusion: This study presents a comprehensive topic map that captures the complexity and cross-sectoral nature of women’s health and provides a unifying framework for the field. The prioritized questions offer a strategic foundation to guide future global research, policy, and investment to advance women’s health innovation…(More)”.

Article by Oliver Wise: “Cities are under growing pressure to make better use of data and artificial intelligence to deliver results for residents. And the dominant advice has been consistent: First, lay the groundwork by standardizing your data, setting up governance processes, and investing in central data stores. Only then, the thinking goes, can local leaders begin to roll out more ambitious, data-driven solutions on challenges from housing to public safety to infrastructure.

But the imperative to show value and improve lives in tangible ways is too urgent to wait for all of that groundwork to take hold, which is why the most effective data teams are taking a different path. They start with urgent problems, demonstrate results quickly, and develop lasting capacity along the way. In this model, capacity is not a prerequisite for action. It is a by-product of it.

Ultimately, this is how local leaders can show residents what’s possible and build their support for using data and AI in more ambitious ways in the future.

As executive director of the Bloomberg Center for Government Excellence at Johns Hopkins University (GovEx), I work every day to help cities navigate this tension of when to invest and when to act. As I do so, I lean on my own experience in city hall.

In 2010, five years after Hurricane Katrina flooded 80 percent of New Orleans’ housing stock, the city was still grappling with tens of thousands of blighted properties. These homes depressed property values, discouraged people from returning, and attracted crime. Residents who had fought insurers and navigated rebuilding programs were frustrated to see their neighborhoods still struggling despite their efforts.

When Mayor Mitch Landrieu took office that year, he set an ambitious goal: reduce blight by 10,000 addresses, about a quarter of the total, by the end of his first term. I was asked to lead a data team to support that effort.

Conventional wisdom would have suggested building strong data governance, management systems, and infrastructure before attempting to deliver results. But we didn’t have the luxury of time. The problem was urgent, and waiting years to get the foundations right would have meant years more blight, declining trust, and missed opportunity.

So we reversed the sequence. We focused on solving the problem first.

We created BlightStat, a performance management program that used data and analytics to guide city action, such as “nudging” homeowners to improve their properties after 311 complaints were filed against them. That approach yielded demonstrable results, and with that momentum, we ramped up the sophistication and impact of our data interventions. We built a machine learning-based recommendation tool to help code enforcement officials determine the most effective path for each property. We used A/B testing to understand which interventions best compelled property owners to act. And we developed civic applications that made the remediation process more transparent to residents, helping rebuild trust in city government…(More)”.

Report by Anna Colom, Elena Murray, and Marta Poblet: “On the diagnosis side, our review identifies multiple, interconnected challenges. Namely: Generative AI providers scrape journalistic content at massive scale while returning negligible traffic or compensation. AI bot traffic also imposes disproportionate infrastructure strain, which is even harder for smaller organisations to cope with. Meanwhile, although exact figures on declining traffic vary, ‘zero-click’ searches are diverting audiences away from publishers. Together, these trends endanger the business models of news organisations, already walking on thin ice following the platformisation initiated by social media companies. Likewise, opacity in Generative AI models and outputs, inherent errors in both content accuracy and attribution, the limitations of Generative AI to summarise journalistic content in context-specific and nuanced ways, and the bypassing of original sources and editorial gatekeepers risk undermining the integrity of information as a key pillar for democracy. The growing concentration of Generative AI power in a few hands, as well as concentration in ways information is generated (with English-centric and large national media favoured over more local or diverse sources), further erodes pluralism, equity, and diversity…(More)”.

Field Guide prepared by Denice W. Ross and Christopher Steven Marcum: “The purpose of this guide is to provide a more complete context for federal data users and stakeholders that will inspire them to consider a broader range of data types in their research and advocacy; we also hope it will also inform national dialogues about the future of federal data.

Whatʼs included? The Guide is organized into eight primary categories of federal data (described on the
right), each representing distinct collection methods, policy frameworks, and use cases. This field guide focuses primarily on publicly available datasets created, maintained, and published by executive branch agencies of the federal government. This Guide does not include sensitive or classified datasets, or
derivative works such as reports or interactive web tools that use data…(More)”

Guidance Note by the Council of Europe: “Providing for unimagined opportunities, at scale and at speed, Generative AI also presents growing risks for freedom of expression and democratic processes, including with regards to the fragmentation of the information space through hyper-personalised experiences, and the lack of transparency, accuracy, repeatability, reliability, and the potential for bias and manipulation, of AI-generated content. The Guidance Note addresses these issues looking into the specific impact on the right to freedom of expression, enshrined in Article 10 of the European Convention on Human Rights.  

Firstly, it outlines the key characteristics of Generative AI technology and its lifecycle, by providing a shared vocabulary and offering a compass for its analysis. Then, the Guidance Note explores how Article 10 of the European Convention on Human Rights and the case-law of the European Court of Human Rights can guide the protection of freedom of expression in the context, and across the lifecycle, of Generative AI. Thirdly, it identifies the structural implications that, both at an individual and societal level, affect the foundations of freedom of expression. Standardisation of expression, hallucination, deep fakes, voice cloning, disinformation and opinion manipulation, being only some of known use cases. Finally, to ensure that Generative AI applications, their design and use, uphold and promote freedom of expression, the document delivers a concrete set of actionable measures for policymakers and other relevant stakeholders, through an agile governance cycle built on four interlocking areas: observe, assess, enable and empower…(More)”.

Framework by Helen McElhinney, Anjali Mazumder, Michael Tjalve, Suzy Madigan, and
Sarah Spencer: “SAFE AI is governance infrastructure for humanitarian AI. It guides organisations through a four-stage Implementation Journey from problem definition to deployment and monitoring, applies a three-tier risk classification with proportionate obligations at each tier, and sets formal Decision Gates at each stage where progression is tested against humanitarian principles, protection requirements, and responsible-refusal conditions.

It does this through a set of named tools deployed at specific points in the lifecycle:

• SAFE AI Onboarding and Readiness Checklist: at problem definition, to establish whether the conditions for responsible AI use exist.
• SAFE AI Impact Assessment: at the first and second Decision Gates, to test whether the use case should proceed.
• SAFE AI Architecture and Procurement Guides: at design and procurement, to secure right-to-audit, model change notification, data ownership, and exit conditions before deployment.
• SAFE AI Technical Assurance: at development and ongoing, to verify performance against documented baselines.
• SAFE AI Transparency Card: the central governance record, documenting decisions, risks, and safeguards across the lifecycle…(More)”.

News release by Smart Data Research UK: “… launched its new data catalogue at the Digital Footprints Conference, making it easier than ever for researchers to find and access smart datasets from across our six data services.

The catalogue (beta) brings together datasets covering finance, energy, transport, health, imagery and more in one searchable place. Researchers can filter by data type, theme, and access conditions, then follow direct links to access the data.

Featured datasets include Zoopla property rental listings, synthetic electric vehicle charging session data, and Spend Dynamics — microdata on household expenditure and consumer finances. Search the catalogue…(More)”.