Stefaan Verhulst

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)”.

Report by UK Government: “…presents insights generated through the Smart Data Challenge Prize. It brings together analysis prepared by participating innovators, alongside overarching observations from the Department for Business, Innovation, Science, and Trade (BIST).

All figures, estimates and supporting material in the finalists’ report sections originate from participating innovators’ submissions. BIST has applied editorial standardisation for clarity and consistency but has not independently validated these claims.

The report should therefore be read as an evidence pack: it describes proposed use cases, finalist experiences and indicative estimates of impact, rather than a formal BIST’s assessment of outcomes…(More)”.

Article by Aaron Martin and Quito Tsui: “In 2019, the World Food Programme partnered with controversial analytics firm Palantir to develop a logistics management platform (DOTS). Although it drew criticism from privacy activists and donors, the partnership remains active today, suggesting that public and sectoral objections cannot halt such public-private collaborations, even in the humanitarian sector. This is but one example of the ways humanitarian infrastructures over-rely on private technology, which depends on a few key actors, or—as in the case of low earth orbit satellites—a single one. Ongoing tech-facilitated violence in Gaza, Iran, and Ukraine, the ascendancy of generative AI platforms amid subdued or outpaced regulation, and larger shifts in the geopolitical order all raise new questions about what will come of the humanitarian embrace of tech.

These choices are being made amid fears of a disintegrating humanitarian sector, torn apart by the severance of USAID funding and cuts elsewhere. Read any article or blog post on the sector and the question, whether implicit or explicit, is the same: What kind of humanitarianism will emerge from this momentous upheaval? To some, tech—automating workflows, shifting to remote modalities, or integrating systems—seems like a good way to stretch the humanitarian budget while retaining a semblance of business as usual. But this intense datafication introduces a new scale of risk, as made apparent by the June 2026 disclosure that the World Food Programme’s beneficiary self-registration system in Gaza had been breached, affecting the data of over 2 million vulnerable people. It also beckons a digital dependency that overrepresents corporate technology, given that the sector lacks many of the resources required to undergo this transformation on its own. Reliance on these companies and their platforms may, however, deepen the usurpation of humanitarian actors by corporate-owned and operated services.

The chosen technologies change how humanitarian work is operationalized. Sidelined and diminished humanitarian agency means the sector’s work becomes susceptible to other tendencies embedded within technology. Many of these tools require or enable the collection of vast amounts of data, tracking and tracing the movement and decisions of those interacting with humanitarian assistance. This extensive data collection and monitoring gives rise to what some experts have called the surveillance-humanitarian complex and others characterize as platform humanitarianism…(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)”.

Article by Henry J. Farrell, Alison Gopnik and James Evans: “The Industrial Revolution was the birthplace of social science as we know it. Entire disciplines such as sociology, political science and economics came into being to analyze enormous social challenges and guide public debate on how to respond. Other social sciences such as psychology and cognitive science followed to study closely how the effects of this revolution were changing us at the individual level.

As we face a new revolution ushered in by artificial intelligence, these disciplines are poorly situated to provide guidance. The Trump administration has slashed federal funding for the social and behavioral sciences, while private funding for certain A.I.-related work has skyrocketed. The result is that many social scientists have left academia and traditional research institutions for A.I. firms that are effectively building out their own social science programs — which threaten to favor the ambitions of A.I.’s creators over the public’s interests.

To navigate the shock waves of A.I., we must restore social science research as an enterprise that starts from the public interest…(More)”.

Guidance by Creative Commons: “Since 2002, Creative Commons (CC) licenses have served as legally enforceable tools for digital sharing. Beyond their functionality, they also signal a commitment to open sharing in the public interest. 

CC’s general guidance has been to acknowledge and support all valid uses for each of our current six licenses and two public domain tools.  

When making decisions about which CC tool to use, we’ve long encouraged licensors to share their work under the most flexible CC license or tool applicable, while recognizing that some contexts call for added restrictions. This practice fosters robust knowledge and cultural exchange, which in turn strengthens our collective commons. Access to educational, scientific, and cultural content remains essential to society. 

However, that guidance was developed for a world of reuse by people, a premise that doesn’t fit as neatly in a world of widespread machine use of content through AI. 

So we went back to our own guidance and asked, “Does this still hold up?”

The short answer is: yes…(More)”.

Paper by Jessica M. Silbey and Woodrow Hartzog: “The term “AI slop” has become popular to describe the output of generative AI systems seen as voluminous, low quality, or the result of little effort. When AI-generated music and videos flood platforms, they are called slop. Peer-reviewed journals and legal tribunals are drowning in low-quality and low-reliability AI slop submissions.  Employees are seen to be producing mountains of slop in their reports and communications with each other. The term has inertia and heft, and the phenomenon has significant consequences. Most of them are not good.

But the boundaries of “AI slop” and its usefulness in policy discussions are not clear. What distinguishes slop from other machine-aided human authored work? Does the concept of “AI slop” help policymakers and researchers better understand a problem or identify a solution? This Article interrogates the concept of AI slop to identify its essential features and document how it operates in three cultural domains where it is commonly deployed—creativity, research, and work. 

We propose a provisional definition of slop as any output of a probabilistic automated system produced with little exertion that asymmetrically burdens recipients and tends to degrade cultural domains. We also conceive of AI slop along a spectrum. Works can be more or less “sloppy,” depending on the relative presence of its three constituent parts: negligible exertion, asymmetrical imposition, and domain degradation. We conclude that AI slop is a useful and important frame in AI discourse and policymaking because it provides a shorthand to distinguish harmful, inferior AI output from meaningful human authorship and because it highlights the pathologies of generative AI for social institutions…(More)”.

Article by Stefaan Verhulst, Jordan Harris, Andrew J. Zahuranec, Maui Hudson, Chantal Kamgne, and WarīNkwī Flores: “Indigenous communities face two related threats from AI: marginalization from digital advancement and extraction of their cultural knowledge and language without consent or benefit. One way to address these challenges is to create pathways for Indigenous communities to exercise meaningful control over how their data is collected, governed, and used.

Ahead of the International Day of the World’s Indigenous Peoples, The GovLab hosted a webinar featuring Indigenous experts Chantal Kamgne (Localizzz / EngageAfricaNLP), WarīNkwī Flores (Kinray Hub / Kara::Kichwa), and Maui Hudson (Te Kotahi Research Institute / Local Contexts) to explore how data commons can offer a middle path toward true data self-determination. The webinar was moderated by Stefaan Verhulst (The GovLab), and Guilherme Canela de Souza Godoi (UNESCO) provided closing remarks.

Three major themes emerged from that discussion, which we detail below: the ability of data commons to serve as a tool for self-determination; how there can be one-size-fits-all solution to governance because such arrangements become a power instrument of top-down pressure; and the need for large institutions to “walk the talk” when it comes to supporting Indigenous communities to take control of their data…(More)”.