Stefaan Verhulst

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)”.

Paper by Joseph Donia, Klaus Hoeyer, Thomas Reintjes and Luca Marelli: “In 2025, the European Union (EU) adopted the European Health Data Space Regulation (EHDS)–a new legal framework for health data. Beyond enabling patient-level and cross-border access to health data for medical care, the EHDS also establishes infrastructures and governance mechanisms oriented to promoting health data reuse by a broader range of actors, including policymakers and industry. A growing literature base suggests that the EHDS involves highly controversial elements, yet it received little attention during its development period. In this article, we examine what makes the EHDS absent in some places and present in others. We engage with perspectives from Science and Technology Studies to reflect on the concept of absence, including the basis for making claims about a particular policy initiative not receiving adequate attention. We do so from a cross-European perspective informed by interviews (N = 27) with participants across 10 European countries, participant observation, and analysis of media coverage in four EU member states. We suggest understanding how a policy initiative can be present in some places and absent in others through four kinds of ‘distance’: temporal, experiential, affective, and scalar. Approaching absence as a social achievement rather than a non-event points to potential avenues through which scholars and others might intervene…(More)”.

Blog by Fernando Monge: “Valuable datasets remain buried or locked in formats and systems that render them unusable, so making this data legible is a direct way to create value. Just mapping the invisible city – the hundreds of miles of covered cables, pipes, and infrastructure laying beneath the urban surface – can save millions of dollars. The UK government estimates that its National Underground Asset Register could generate more than £400 million in economic benefits each year by reducing accidental strikes on underground infrastructure.

But making this data usable is expensive. Some governments lack the resources to clean, process and prepare this data for dissemination. If they have to publish it openly and cannot recover the costs, this work can appear as another unfunded item in an already tight budget. An item that never makes it into the priority list, so rich data remains locked in closed systems, when not in physical archives.

This is the issue that the UK’s proposal on charging for certain public-sector data reuse is trying to tackle.

And it’s not merely a technical issue. Decisions on how to fund open data have deep implications on who pays, who benefits, and what are the terms of access to a critical infrastructure on top of which thousands of products and services – public and private – are built…(More)”.

Paper by Jason Brennan and Christopher Freiman: “Many democratic theorists want to replace electoral democracy with sortition or lottery-based political systems, in which at most a small number of lottery-selected citizens will be appointed to exercise political power. They claim one virtue of these systems is that they tend to ensure demographic proportionality, while electoral democracy, they claim, has uneven demographic turnout in voting and even more uneven demographic results in elections. Lottocrat propose to disenfranchise nearly all citizens, but claim, no matter, citizens can be assured that someone demographically like them will hold power. This paper argues that there is something morally problematic about treating individual citizens as tokens of their demographic groups, even if, empirically speaking, demographics have great predictive power about how people vote or what political outcomes they prefer…(More)”.

Article by Steve Newman: “AI progress is racing along, but virtually all of the visible progress is in the realm of knowledge work, i.e. activities that can take place inside a computer.

In the San Francisco AI scene, there is a widespread belief that robots will soon enter the picture. In parallel with the race to develop broadly capable AI, there is an equally aggressive race to develop broadly capable robots – humanoid machines imbued with physical intelligence. Artificial workers that can cook and clean, fetch and carry… and do everything else, including building more of themselves, leading (in many forecasts) to economic growth best characterized as an “explosion”.

In other words, the thinking goes, AI in the data center will soon subsume all intellectual labor, and AI in humanoid bodies will soon subsume all physical labor. However, there is an important difference: while we can see progress in the intellectual realm, the physical side of AI is mostly confined to test facilities and demo videos. There is no robot equivalent to ChatGPT – nothing that you or I, or even most people in the AI community, can get our hands on.

So we’re stuck with demo videos. Unfortunately, they are a poor tool for assessing progress. We might be seeing the one successful task achieved in 100 attempts. The scenario might have been carefully arranged to avoid challenges the robot isn’t ready for. The video might be edited to make it look like the robot is acting with more speed and reliability than is actually the case. Here’s one very impressive demo… with a suspiciously large number of camera cuts…

Demos draw attention to the things a robot can already do. The question then becomes: what’s missing? In today’s post, I’ll catalog the technical challenges that will have to be overcome along the road to broadly capable artificial workers. The next time you watch a robot doing something impressive, ask yourself: which of these capabilities has the robot demonstrated, and which challenges might the demo scenario be avoiding?..(More)”.