Stefaan Verhulst
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)”.
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)”.
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)”.