Stefaan Verhulst
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)”.
Paper by Michael Dalton, Lisa B. Kahn & Andreas I. Mueller: “We ask whether online job postings capture U.S. job vacancies, matching near-universal Burning Glass (BG) postings to the representative Job Openings and Labor Turnover Survey (JOLTS). BG converged toward JOLTS over 2007–2022, and its representativeness varies across establishment characteristics. We reweight BG to align postings with JOLTS openings along observables, and assess impacts on prior BG findings. Because large establishments account for most openings and are well represented, the level and cyclicality of skill demand survive our adjustment. But BG overstates labor market concentration (the Herfindahl-Hirschman Index) nearly threefold, because small and mid-sized establishments post less online…(More)”.
Paper by Mustafa Karatas et al: “Hybrid-capture wastewater sequencing has shown promise for episodic viral epidemiology, but its value for multi-year, multi-pathogen, strain-resolved surveillance benchmarked against clinical data remains unclear. Here we applied hybrid-capture viral metagenomics to 127 weekly 24-h composite influent samples from a single sentinel wastewater treatment plant in Leuven, Belgium, collected over 31 months, and linked wastewater time series to national clinical indicators of multiple pathogens. Our approach recovered a genome-resolved target-enriched virome spanning 339 viral species and 715 strains, captured seasonal community turnover and showed that rotavirus A in wastewater typically preceded clinical surveillance by ~1 week. It also identified signals beyond routine wastewater programmes, notably a parvovirus B19 surge starting in April 2023, about 1 year before the national public health alert. Overall, here we show that longitudinal hybrid-capture metagenomics can extend wastewater surveillance from detection to strain-resolved, multi-virus epidemiology and can provide early circulation signals for clinically relevant viruses…(More)”.
Article by Adam French: “…Bank of England Chief Economist Huw Pill reveals the Monetary Policy Committee (MPC) is using AI, web-scraping and real-time financial data to improve its understanding of the economy – with information collected by Moneyfacts among the private-sector data used to inform its assessment.
The Bank of England is turning to artificial intelligence and new sources of real-time financial data to identify economic signals that could otherwise be missed, Chief Economist Huw Pill has revealed.
Pill says the Bank is already using AI models to extract quantitative signals from qualitative information, including corporate reports, survey responses and conversations between the Bank’s regional agents and businesses.
The Bank is also using web-scraping and other technologies to develop new sources of information for monetary policy, while data science is allowing policymakers to analyse large datasets. This includes anonymised real time data on individual mortgages and bank accounts.
“We have been using AI models to draw more quantitative signals from the qualitative data coming from corporate reports, survey responses, or agents’ conversations with their business contacts across the country.”
Pill says the technology is helping the Bank distinguish genuine economic signals from louder and more volatile data. One of the biggest challenges facing modern monetary policymakers. “Extracting signal from noise has become even more important (and difficult) over the past few years.”…(More)”.