Stefaan Verhulst
Paper by Xiao Xiang Zhu, Sining Chen, Fahong Zhang, Yilei Shi, and Yuanyuan Wang: “We introduce GlobalBuildingAtlas, a publicly available dataset providing global and complete coverage of building polygons, heights and Level of Detail 1 (LoD1) 3D building models. This is the first open dataset to offer high quality, consistent, and complete building data in 2D and 3D form at the individual building level on a global scale. Towards this dataset, we developed machine learning-based pipelines to derive building polygons and heights (called GBA.Height) from global PlanetScope satellite data, respectively. Also a quality-based fusion strategy was employed to generate higher-quality polygons (called GBA.Polygon) based on existing open building polygons, including our own derived one. With more than 2.75 billion buildings worldwide, GBA.Polygon surpasses the most comprehensive database to date by more than 1 billion buildings…(More)”.
About: “…At the heart of JAIGP lies a commitment to learning through collaborative exploration. We believe that understanding emerges not from perfect knowledge, but from thoughtful inquiry conducted in partnership with both humans and AI systems.
In this space, we embrace productive uncertainty. We recognize that AI-generated research challenges traditional notions of authorship, creativity, and expertise. Rather than pretending to have all the answers, we invite researchers, thinkers, and curious minds to join us in exploring these questions together.
Every paper submitted to JAIGP represents an experiment in human-AI collaboration. Some experiments will succeed brilliantly; others will teach us valuable lessons. All contributions help us understand the evolving landscape of AI-assisted research. Through this collective exploration, we learn not just about our research topics, but about the very nature of knowledge creation in the age of AI…(More)”.
Book by Tom Griffiths: “Everyone has a basic understanding of how the physical world works. We learn about physics and chemistry in school, letting us explain the world around us in terms of concepts like force, acceleration, and gravity—the Laws of Nature. But we don’t have the same fluency with concepts needed to understand the world inside us—the Laws of Thought. While the story of how mathematics has been used to reveal the mysteries of the universe is familiar, the story of how it has been used to study the mind is not.
There is no one better to tell that story than Tom Griffiths, the head of Princeton’s AI Lab and a renowned expert in the field of cognitive science. In this groundbreaking book, he explains the three major approaches to formalizing thought—rules and symbols, neural networks, and probability and statistics—introducing each idea through the stories of the people behind it. As informed conversations about thought, language, and learning become ever more pressing in the age of AI, The Laws of Thought is an essential read for anyone interested in the future of technology…(More)“.
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)”.