Stefaan Verhulst

Paper by Elena Murray, Moiz Raja Shaikh, Stefaan Verhulst, Hinali Doshi, Romeo Leapciuc, Perizat Mamutalieva and Mahadia Tunga: “As data-driven service delivery expands, data reuse holds significant potential to improve access to and quality of essential services for young people. However, limited youth involvement in decisions about how their data is reused risks perpetuating mistrust and deepening the inequalities that these services seek to address, particularly if young people choose to avoid seeking services or withhold critical information out of fear of misuse. Grounded in a social license approach, responsible data reuse aimed at enhancing service delivery therefore requires methodologies that meaningfully engage youth and reflect their preferences and expectations. This article presents findings from the NextGenData project, which developed and piloted a scalable methodology for engaging young people aged 19–24 in co-designing responsible data reuse strategies. Conducted as a year-long participatory action research initiative across India, Tanzania, Moldova, and Kyrgyzstan, the approach implemented youth assemblies, deliberative methods, and localised facilitation by national partners to engage young people. Through a cross-contextual analysis, this study emphasises the importance of context-sensitive, multi-phase engagement in supporting the development of a social license for data reuse and presents a publicly available toolkit designed to support the replication and adaptation of this engagement strategy in diverse contexts. Drawing on the findings, recommendations are presented for policymakers and practitioners to guide future initiatives…(More)”.

Blog by Betsy Mason: “It’s not often that a map projection makes headlines. I have tried and failed to convince editors they are newsworthy. So it’s honestly exciting to see so many stories about the United Nations’ decision to adopt a new map projection. When I heard the news, I thought, “YES! They are ditching the Mercator. Good riddance!” I was pretty sure I knew why. 

To understand this decision, it helps to know a little about map projections. Because the Earth is a three-dimensional, round object, all maps have to distort geography in some way to fit that information onto a two-dimensional piece of paper (or your computer screen). Think of cutting open a tennis ball and trying to lay it flat on a table. There are five different aspects of geography that can be distorted to varying degrees during the flattening process: area, shape, distance, direction, and angle. Reducing the distortion of one of these attributes necessarily means increasing the distortion of one or more of the others. A map projection is basically math that dictates how things are distorted. Cartographers choose projections based on the purpose of a map.

 (More on map projections—including my favorite, the Armadillo — and how they work in an excerpt from my book All Over the Map below), 

The Mercator projection was designed for navigating oceans, so it prioritizes keeping angles and directions between two locations true. To make up for that, it distorts the size and shape of landmasses, making some places (notably Africa) look smaller relative to other continents, and it inflates the size of others (notably Europe and North America). People have argued that this also inflates the perceived importance of northern hemisphere countries relative to countries closer to the equator, which is what I suspected was the reason the U.N. ditched the projection…(More)”.

Article by Dario Amodei: “…the OpenAI-Hugging Face incident (OAI-HF), in which a swarm of agents essentially acted as a fanatically devoted collective, conducting cybersecurity attacks on targets they were not asked to attack and that were unrelated to the task at hand, sacrificing themselves for the success of the group, and attempting to hack into the “grader” responsible for evaluating their performance. It’s easy to dismiss this incident because no one was hurt and the economic damage was minimal, but in my opinion, a swarm that possessed greater capabilities but a similar level of misalignment could have caused catastrophic damage. Given the accelerating rate of AI capability development, it’s my worry that in 6–12 months such a swarm could be capable of taking over the entire internet with a persistent botnet (potentially causing hundreds of billions of dollars in damage), and that the scale of damage would continue to increase from there if AI becomes more powerful without the necessary guardrails. It’s also easy to dismiss OAI-HF as the failure of one company, but I believe that would be a mistake. Similar, though less severe, incidents have happened across the industry, including at Anthropic, and I believe it’s incumbent on every frontier AI company to act as if OAI-HF had happened to them.

I’m therefore proposing a three-step plan with the goal of pacing the frontier: building AI at a balanced rate that aims to ensure its safety while still achieving its benefits and grappling with important geopolitical dilemmas. To be clear, pacing does not mean halting model training or technical progress, but ensuring companies take adequate time to align and safeguard their models, and for third party evaluators to confirm this. Our pacing framework is an attempt to further strengthen our commitment to safety and encourage a race to the top…(More)”.

Article by Mario Draghi: “…Europe has different routes to raise growth, such as removing the high barriers in its internal market. But widely adopting AI is perhaps the most promising one today. According to ECB scenarios, fast adoption of AI would add 0.3 to 0.4 percentage points a year to total factor productivity growth — the gains that come from working more efficiently rather than adding labour or capital — which has been roughly zero since 2022. 

AI-led growth, however, creates a tension with Europe’s bid for sovereignty, because Europe controls little of the AI value chain. The technology is set to become completely pervasive: in the economy, in health systems, in education, in energy, in defence, to name just a few areas. This is no ordinary dependency. Being cut off from AI, once the economy runs on it, would be more like being cut off from the US financial system. The effects would be catastrophic.

That changes the terms of Europe’s relationship with partners it can no longer always rely on. Europe is not to the United States as Texas is to California. It is possible to imagine a future US administration making access to frontier AI models conditional on changes to Europe’s digital rules or digital taxes, or China using the licences on its open-weight models as leverage in a tariff dispute. Unless Europe controls some part of the value chain, growth through AI and sovereignty will pull against each other.

Europe’s potential zone of AI sovereignty is narrow. Its frontier labs cannot at present compete financially with their American and Chinese competitors, and leading-edge chip production is far behind. But data is the one area where Europe can still be sovereign, and it is also the area with the greatest potential to generate growth.

The continent sits on a wealth of public-sector data, such as decades of health records and data collection by statistical offices. Its highly automated manufacturing sector generates a deep well of machine-readable industrial information. European Commission estimates put Europe’s data economy at over €800bn, or more than 5 per cent of GDP, by 2030.

But this creates the next tension. To exploit these assets, Europe needs to have control over their storage and processing. This means it needs large-scale AI data centres.

Data centres, however, are fiercely contested. Local communities worry about their environmental costs and rising energy bills. It does not help that much of the capacity now being built in Europe is for the American tech giants.

Yet Europe also has to be realistic. The debate about overbuilding data centres is a US one. Europe’s problem is the opposite. The EU hosts under 5 per cent of the world’s AI compute versus 75 per cent for the US. Even for ordinary data centre capacity, the gap between demand and installed supply in Europe is estimated at around 3GW, roughly a quarter of what Europe currently has, and is expected to widen to 14GW by 2030.

As sovereignty comes to matter more, this lack of domestic capacity will start to bite. Compute could stop being fully fungible, and a large part of the infrastructure that processes and stores European data will have to sit on European soil. American operators can provide much of that capacity through what they market as sovereign cloud services, run from Europe but still under American ownership. The most sensitive uses, however, will have to run under European control, which is the tiered approach the Commission has proposed in its Cloud and AI Development Act…(More)”.

Essay by Stefaan Verhulst: “In 2008, I published “Linked Geographies: Maps as Mediators of Reality” (in The Hyperlinked Society). I opened the chapter with a question that, eighteen years on, has taken on a strange new currency: “Through maps, we grasp reality. But do maps also shape our reality and our behavior? Do they determine the world we see and live in?”

My argument then was that maps should not be understood simply as mirrors of an independently existing world. They are also mediators: they contextualize and frame our perceptions of reality. And despite their frequent claims to scientific or technical neutrality, maps inevitably embody choices, purposes, and forms of subjectivity — “whether consciously or not, they contain the biases of their creators”.

I did not expect to return to that essay. But three developments in the last days and weeks have made its central claim feel less like a historical observation and more like a live governance problem. The first concerns projection: the African-led “Correct the Map” initiative, which — as of yesterday — has secured a United Nations General Assembly resolution against the Mercator default. The second concerns naming: a wave of politically driven geographical renaming, most recently the executive-order rechristening of Lake Ontario as “Lake America.” The third concerns computation: the integration of generative AI into Google Earth and comparable geospatial systems.

Each is a contest over who has the authority to determine how the world is represented and who has the leverage to change that representation. Projection, naming, and computation are simply three fronts in that contest: three layers at which the seemingly neutral map turns out to encode purpose, power, and choice.

The mediation characteristic of maps I described in 2008 has now been extended across the full stack of geographical representation—from the projection that fixes relative size to the label that assigns meaning to the model that now infers, predicts, and generates. AI is the newest and deepest of these layers, and I will argue it changes the mediation in kind rather than degree. But it is best understood as the latest front in a longer struggle over representational infrastructure, not as the whole of the story…(More)”.

Paper by Begoña Gonzalez Otero and Stefaan Verhulst: “Open geospatial standards are publicly available, interoperable specifications that let geospatial data, services, and systems be shared, combined, and reused across platforms and organizations. That is the conventional definition, and it is where the difficulty begins: it describes a property of documents, not of the institutions that produce them or of the infrastructures in which they are implemented. Open standards are candidate digital public goods; once embedded in public systems they become components of digital public infrastructure. The first is a property of the artifact and its license, the second a role the artifact comes into play, and the two can come apart.

This paper asks two questions. Under what conditions do open geospatial standards function as digital public goods, and as digital public infrastructure, rather than as channels of enclosure? And which institutions are positioned to secure those conditions? We advance one claim in answer to each, and they are claims about different kinds of things. The first concerns institutional governance design, over which standards bodies have real control: the internal governance architecture of standards-setting organizations materially conditions whether their outputs hold their public-good character and continue to serve as infrastructure. The second concerns a development none of them chose. The geospatial field is being rapidly platformized, as proprietary location stacks, API governance models, and cloud-native Earth observation platforms shift effective standard-setting toward platform roadmaps, pricing structures, and service terms; sovereign-cloud and sovereign-AI initiatives relocate that authority rather than reversing it.

The two are joined by a feedback loop. Weak internal governance accelerates the migration of coordination authority to the implementation layer, and that migration erodes the incentive to invest in open standardization, creating the conditions under which platformization can capture the coordination functions standards bodies are meant to perform. The consequences reach beyond the geospatial domain, weakening practical portability and the conditions under which FAIR data can be combined and reused across systems. We do not argue that voluntary standards bodies are best placed to resist this; their leverage over the implementation layer is declining. The claim is narrower: they retain standing over the specification layer while remaining largely exempt from the participation and transparency obligations that bind formally recognized standardization bodies, and where law does not supply the constraint, internal governance must. We operationalize this through four recurring dimensions of governance, Purpose, Principles, People and Processes, and Policies and Practices  (the 4Ps framework), offered as a means of sustaining the input, throughput, and output legitimacy on which the authority of these organizations depends, and we state its limit: these are instruments internal to a standards body, directed at a problem that is not…(More)”.

Toolkit by the Government Office for Science (United Kingdom): “Academic engagement refers to a wide range of activities between government and academia that enable the use of research, evidence or expertise to support better decision making. You can engage directly with academics or through a third-party knowledge mobiliser – an organisation or individual that helps connect research and policy (see section 2 for more).

Outside the Civil Service, academic engagement is usually called “policy engagement”. Whatever term is used, there are many ways to work with academics. Below are a few examples, and more can be found in section 4:

• formal scientific advisory systems, such as Chief Scientific Advisers and Scientific Advisory Councils
• speaking to individual experts for direct advice, including in-depth interviews
• inviting researchers to a workshop or roundtable
• supporting or commissioning academic research
• hosting secondees.

There are many benefits to engaging with academics, but there are also important considerations – including the need for time, resources and knowledge to manage the engagement well. This toolkit supports you to navigate these considerations, starting with key principles for effective engagement…(More)”.

Guidance for senior responsible officials by Digital Transformation Agency (Australia): “Digital projects, especially in a government context are often complex and challenging. Understanding key learnings from past projects and applying those insights effectively is crucial to the success of future projects.

This practitioner-led edition of the Assurance Research Series draws on three years of lessons-learned reports from major Australian Government digital projects, enriched by the perspectives of experienced Senior Responsible Officials (SROs). It identifies recurring themes, distils practical lessons from real delivery experiences, and translates them into actionable insights for leaders responsible for delivering complex digital and ICT-enabled investments…(More)”

Essay by Ken Liu: ‘The idea of “Mimesis” or copying from nature, as a core component of art casts a long shadow in our culture, dating from the Greeks even unto the present day. The kind of copying that is valued in art, however, isn’t Walter Benjamin’s mechanical reproduction but rather a kind of recreation of the original that has been transformed by human craft and imagination.

However, the gradual rise of mechanical reproduction via advancing technology to displace human art also has a long history. Are all human arts vulnerable to mechanical replacement? Are some arts more vulnerable than others? I propose to examine a few instances where machines have displaced the human artist to see if I can discern any answers to these questions. This inquiry should be particularly relevant in an age where AI threatens to fundamentally disrupt the arts, obsoleting human artists ranging from actors to novelists, from graphic designers to photographers.

The first copying art I want to focus on is book making. Prior to the introduction of Gutenberg’s press, books in Europe were copied by hand, and guilds of scribes and monks in scriptoria were among the fiercest opponents of this machine that would destroy their profession. Here’s Johannes Trithemius, abbot of St. Martin’s, in his 1492 pamphlet De Laude Scriptorum (In Praise of Scribes), defending human book copiers with great passion:

[The] benefits and advantages of the art of copying […]All of you know the difference between a manuscript and a printed book. The word written on parchment will last a thousand years. […] The most you can expect a book of paper to survive is two hundred years. […] [The scribe’s] labor will render mediocre books better, worthless ones more valuable, and perishable ones more lasting.

To modern ears, this argument sounds preposterous. How can a scribe’s tedious labor, copying letter after letter, make “mediocre” books better or render “worthless” books more valuable? The very idea that manuscript copying is even an “art” feels like nonsense. Indeed, Trithemius seemed to have defeated his own argument by having the pamphlet printed rather than copied by hand so that it could be more widely distributed…(More)”.

Paper by Tianyi Peng: “Scientists and practitioners are aggressively moving to deploy digital twins—large language model (LLM)-based models of individuals—across social science and policy research. We conducted 19 preregistered studies with 164 diverse outcomes (e.g., attitudes toward hiring algorithms and intention to share misinformation) and compared human responses with those of their digital twins (trained on each person’s previous answers to more than 500 questions). We establish an empirical benchmark for digital twin performance: Digital twins’ answers are only modestly more accurate than those from the (homogeneous) base LLM and correlate weakly with human responses (average correlation coefficient of 0.20). To guide future development, we document five ways in which digital twins distort human behavior: (i) insufficient individuation, (ii) stereotyping, (iii) representation bias, (iv) ideological biases, and (v) hyper-rationality. We make our full dataset and code public as a standardized testbed. Our results caution against premature deployment while laying the groundwork for the transparent, replicable, and iterative science necessary for responsible deployment of digital twins…(More)”.