Stefaan Verhulst

The Case for Innovating and Institutionalizing How We Define Questions by Stefaan Verhulst:

“In an age defined by artificial intelligence, data abundance, and evidence-driven rhetoric, it is tempting to believe that progress depends primarily on better answers. Faster models. Larger datasets. More sophisticated analytics. Yet many of the most visible failures in policy, innovation, and public trust today share a quieter origin: not bad answers, but badly framed questions.

What societies choose to ask, or fail to ask, determines what gets measured, what gets funded, and ultimately what gets built. Agenda-setting is not a preliminary step to governance; it is governance. And yet, despite its importance, the practice of defining questions remains largely informal, opaque, and captured by a narrow set of actors.

This is the gap the 100 Questions initiative seeks to address. Its aim is not philosophical reflection for its own sake, but something decidedly practical: accelerating innovation, research, and evidence-based decision-making by improving the way problems are framed in the first place.

Why questions matter more than we admit

Every major public decision begins long before legislation is drafted or funding allocated. It begins with scoping — deciding what the problem actually is. It moves to prioritization — choosing which issues deserve attention now rather than later. And it culminates in structuring the quest for evidence — determining what kinds of data, research, or experimentation are needed to move forward…(More)”.

Resource by Stefaan Verhulst and Adam Zable: “In today’s AI-driven world, the reuse of data beyond its original purpose is no longer exceptional – it is foundational. Data collected for one context is now routinely combined, shared, or repurposed for others. While these practices can create significant public value, many data reuse initiatives face persistent gaps in legitimacy.

Existing governance tools, often centered on individual, point-in-time consent, do not reflect the collective and evolving nature of secondary data use. They also provide limited ways for communities to influence decisions once data is shared, particularly as new risks, technologies, partners, or use cases emerge. Where consent is transactional and static, governing data reuse requires mechanisms that are relational and adaptive.

A social license for data re-use responds to this challenge by framing legitimacy as an ongoing relationship between data users and affected communities. It emphasizes the importance of clearly articulated expectations around purpose, acceptable uses, safeguards, oversight, and accountability, and of revisiting those expectations as circumstances change.

To support this work in practice, The GovLab has now released Operationalizing a Social License for Data Re-Use: Questions to Signal and Capture Community Preferences and Expectations. This new facilitator’s guide is designed for people who convene and lead engagement around data reuse and need practical tools to support those conversations early.

The guide focuses on the first phase of operationalizing a social license: establishing community preferences and expectations. It provides a structured worksheet and facilitation guidance to help practitioners convene deliberative sessions that uncover priorities, acceptable uses, safeguards, red lines, and conditions for reuse as data is shared, combined, or scaled…(More)”.

Chapter by Silvana Fumega: “Feminicide data activism sits at the intersection of gender, power, and resistance. Far from a neutral or technical endeavor, the act of documenting feminicide confronts deeply entrenched norms about whose lives matter, who counts as a victim, and what constitutes security. In contexts where official data is absent, or distorted, feminist communities have mobilized to resist erasure and demand accountability.

Since its inception, Data Against Feminicide (DcF) has not collected feminicide data itself but has instead focused on supporting those who do, by fostering a community of practice, developing tools and training programs, and hosting annual events to strengthen their efforts.

The importance of these efforts becomes even clearer when placed in the context of Latin America, where legal recognition of feminicide has not always translated into justice. The enactment of feminicide legislation in many countries has marked a crucial step in naming and confronting gender-based violence as systemic. Yet, these frameworks sometimes rely on narrow definitions, suffer from weak implementation, or remain siloed within criminal justice systems. As a result, the production of official data remains fragmented and politically constrained; shaped as much by normative assumptions as by legal categories. The existence of a legislation or typification does not guarantee justice, particularly when institutional actors lack the resources or will to apply it meaningfully. This gap perpetuates a dangerous disconnect between recognition and accountability.

In this context, this chapter explores how DcF operates as a community of practice that challenges dominant knowledge systems, confronts harmful media narratives, and proposes alternative, care-based approaches to security through feminist data infrastructures…(More)”.

IFC Publication: “As demand for responsibly sourced materials such as lithium, cobalt, and nickel continues to grow, driven by the global energy transition, robust ESG data practices have become essential. These practices enable mining companies to meet stakeholder expectations for transparency and ensure operations align with sustainability targets. PWC’s 2021 Global Investor ESG Survey found that 83% of investors viewed detailed, evidence-based ESG reporting as critical to maintaining investment appeal. Additionally, regulatory frameworks like the European Sustainability Reporting Standards (ESRS) under the Corporate Sustainability Reporting Directive (CSRD) require companies to conduct a double materiality assessment—evaluating how sustainability issues may financially impact the company and affect people or the environment. Having the right data is critical to addressing these emerging trends. The question we need to ask is whether ESG data is being utilized to its full potential…(More)”.

Google Trends Story by Emily Barone: “Winter months are dreary in New York City, but perhaps none so much as January 2021. Cold air and gray clouds blew between the skyscrapers as the world below remained stuck in the pandemic’s icy grip.

But that month, a small corner of the city briefly came alive when a majestic Snowy Owl appeared in Central Park. Bird fanatics and dozens of other intrigued New Yorkers ventured out of their homes, hoping to catch a glimpse.

As word spread, so, too, did people’s curiosity. In New York City, Google searches for the term Snowy Owl spiked as residents wanted to learn about the species — and how one ended up in their backyard. New York’s Snowy Owl was as much a story about one special bird as the humans who took notice of it. Google search data, which is available through the company’s Google Trends database, can show us which birds capture our attention…(More)”.

OECD Report: “Advances in optical systems, photonics, cloud computing and artificial intelligence have democratised both the quality and accessibility of satellite data. However, this convergence of technologies also carries risks, including for national security and privacy. This blog post identifies emerging challenges in commercial space-based earth observation and how governments can address them…(More)”.

Article by Damilare Dosunmu: “If you speak to an artificial-intelligence bot in an African language, it will most likely not understand you. If it does manage to muster a response, it will be rife with errors. This is an existential problem with AI that everybody in Africa is trying to solve. Now, Google has joined the cause.

On February 3, Google launched WAXAL, a data set for 21 African languages, including Acholi, Hausa, Luganda, and Yoruba.

“Taking its name from the Wolof word for ‘speak,’ this dataset was developed over three years to empower researchers and drive the development of inclusive technology across Africa,” Google said in a blogpost.

While WAXAL will make building AI products that understand African languages easier, it represents a rare move toward digital sovereignty: The data set is owned by African partners who worked on the project, and not Google.

“WAXAL is a collaborative achievement, powered by the expertise of leading African organizations who were essential partners in the creation of this dataset,” Google said. “This framework ensures our partners retain ownership of the data they collected, while working with us toward the shared goal of making these resources available to the global research community.”

Google’s African partners for this project include Makerere University in Uganda, the University of Ghana, AI and open data company Digital Umuganda in Rwanda, and the African Institute for Mathematical Sciences, among others…(More)”.

Chapter by Renée Sieber, Ana Brandusescu, and Jonathan van Geuns: “…draws on examples of governance challenges from the AI in Canadian Municipalities Community of Practice to examine how municipalities navigate artificial intelligence adoption, balance in-house development and outsourcing, and face a gap in public participation. It presents four recommendations, including iterative adoption, stronger collaboration, deeper debate on social impacts, and more civic involvement to strengthen local AI governance…(More)”.

Article by Mike Kuiken: “…This matters beyond accounting arcana because we’re entering an era where data isn’t just valuable — it’s the essential feedstock for AI. Shouldn’t we be able to measure it?

The government dimension makes this even more urgent. Federal agencies sit on extraordinary data holdings: agricultural yields, geological surveys, anonymised health research. A valuation framework could actually strengthen privacy by forcing explicit accounting for data’s worth and clearer protocols for its protection. Right now, federal data policy is a patchwork of inconsistent practices precisely because we have no systematic way to understand what we’re protecting or why.

Assets that aren’t valued aren’t protected. The Office of Personnel Management breach in 2015 compromised the security clearance of 21.5mn Americans. We’ve solved harder problems before: governments have auctioned the electromagnetic spectrum for decades — rights to invisible frequencies that drive billions in economic value — because we decided it mattered enough to measure.

None of this requires adopting China’s approach wholesale. Beijing’s data exchanges serve state priorities. American capital markets demand more rigour. But the fact that China is experimenting while America refuses to engage with the question at all reveals something about strategic intent versus strategic indifference.

The Financial Accounting Standards Board should initiate a project to develop data asset recognition standards. The Securities and Exchange Commission should study disclosure requirements for material data holdings. Congress should mandate that federal agencies assess the value of their data assets. State and local governments should do the same…(More)”.

Paper by Paolo Andrich et al: “Accurate and timely population data are essential for disaster response and humanitarian planning, but traditional censuses often cannot capture rapid demographic changes. Social media data offer a promising alternative for dynamic population monitoring, but their representativeness remains poorly understood and stringent privacy requirements limit their reliability. Here, we address these limitations in the context of the Philippines by calibrating Facebook user counts with the country’s 2020 census figures. First, we find that differential privacy techniques commonly applied to social media-based population datasets disproportionately mask low-population areas. To address this, we propose a Bayesian imputation approach to recover missing values, restoring data coverage for 5.5% of rural areas. Further, using the imputed social media data and leveraging predictors such as urbanisation level, demographic composition, and socio-economic status, we develop a statistical model for the proportion of Facebook users in each municipality, which links observed Facebook user numbers to the true population levels. Out-of-sample validation demonstrates strong result generalisability, with errors as low as ≈18% and ≈24% for urban and rural Facebook user proportions, respectively. We further demonstrate that accounting for overdispersion and spatial correlations in the data is crucial to obtain accurate estimates and appropriate credible intervals. Crucially, as predictors change over time, the models can be used to regularly update the population predictions, providing a dynamic complement to census-based estimates. These results have direct implications for humanitarian response in disaster-prone regions and offer a general framework for using biased social media signals to generate reliable and timely population data…(More)”.