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)”
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)”.
OECD Report: “This report aims to support governments in promoting private investment that advances digital transformation, while considering its implications for long term growth, resilience and societal well being. Building on existing OECD investment standards – including the Policy Framework for Investment, 2015 Edition, and the Foreign Direct Investment Qualities Policy Toolkit – it identifies key policies for enabling investment in digital transformation and harnessing its potential to strengthen economic resilience and broad based development…(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)”.
Paper by David J. Deming, Katrine V. Løken, Alexander Willén & Yaling Xu: “Why do we have so many meetings? Few workplace features are so scorned, yet seemingly so necessary. This paper provides the first large-scale economic evidence on workplace meetings using an original survey of more than 9,000 workers linked to matched employer–employee administrative data from Norway. We show that meetings are both common and costly, consuming an average of 12 percent of work hours and 14 percent of firm wage bills. Planning, problem solving, information sharing, and project coordination account for the majority of meeting activity. High-paying and high-revenue firms devote more resources to meetings despite facing a substantially higher opportunity cost of employee time. Meeting frequency and intensity are positively related to worker wage growth. Workers in meeting-intensive firms report greater on-the-job learning, and interactions with more senior colleagues are associated with stronger wage growth, suggesting that knowledge transmission within firms is an important mechanism. Meetings are the broccoli of work – widely disliked, but probably good for us anyway…(More)”.