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)”.
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)”.
Blog by Geoff Mulgan: “In this piece I describe what I call ‘knowledge twins’, combining data, evidence, foresight, tacit knowledge and more, to enable governments to make better decisions and to learn more effectively. I show how these can build on many existing tools, from evidence syntheses to knowledge graphs, digital twins to systems maps, and how they can help policy-makers realise the full potential of AI. I show how they could evolve over the next decade to make intelligence useful and used, on everything from economic growth to decarbonisation, mental health to poverty, bio and climate risks to the future of care, and become essential cognitive infrastructures for governance.
Understanding what causes what – the grounding for everything governments do
Governments have always been interested in intelligence. But in the past that usually meant secret intelligence about enemies and threats. In the 19th and 20th centuries that interest broadened to include intelligence about the economy, health and population, alongside a recognition that shared intelligence can sometimes be much more valuable than intelligence that is hoarded.
Every part of any government rests on implicit or explicit models of how the world works. If we pass this law, spend money in this way or introduce (or cut) this programme, these are what we think the effects will be. Pre-modern governments were different, as are contemporary autocrats: it might be enough for a law to reflect theology, or the whims of a king or President, or just hope. But in the modern era it’s hard to see why we as citizens would want our money spent, or our freedoms curtailed, for programmes not based on serious assessment of whether they will succeed.
For the same reason much of politics revolves around promises that actions will have predictable effects. So, if a party or candidate promises that their actions can achieve desirable ends such as higher growth, better health, lower crime or immigration, we expect them to be able to say how and we expect to be able to examine their homework to see if it’s convincing.
These causal models get rough and ready scrutiny in an election campaign, and sometimes outside them, mainly through interviews on broadcast and in print media, and on some topics, such as fiscal plans, there are well-resourced independent institutions (like the UK’s Institute for Fiscal Studies or Resolution Foundation) able to scrutinise the assumptions. Indeed, economics is a field where shared intelligence is widely accepted: governments share lots of data, analyses, models and forecasts and have to persuade markets and observers that they know what they’re doing (though of course there are many serious blindspots).
I argue here that these habits of explicit claims, interrogation and openness, should become more normal in other fields, with all departments and agencies seeing themselves as providers of reliable intelligence to the systems they are responsible for. In an era of ubiquitous AI this will be vital for making the most of new generations of technology. But it will also require new institutions, new methods and new mindsets…(More)”.
Book by Martin J. Williams: “Building an effective civil service is crucial for public service delivery and good governance, but reforming bureaucratic institutions is notoriously difficult. This book takes a fresh perspective on this challenge by documenting and analyzing the implementation of more than one hundred reforms initiated by six African countries over the last thirty years.
Martin J. Williams shows that these efforts largely fell short of their goals because they typically approached organizational change as a matter of changing formal structures and processes through one-off projects. Some did yield positive changes, however, when they were able to create opportunities for civil servants to discuss performance and how to improve it. Drawing on this evidence, Williams develops a new theory of how systemic reforms can lead to meaningful change—not by trying to force it through top-down interventions but by catalyzing an ongoing and decentralized process of continuous improvement.
Reform as Process makes theoretical and empirical contributions to research on organizational performance, civil service reform, and public service delivery, and it shares practical insights and strategies to help reformers around the world achieve meaningful change in their organizations…(More)”.
Paper by Georgy Egorov & Konstantin Sonin: “Artificial intelligence is increasingly used for political advice. We study an AI that is better informed about a payoff-relevant state and cares both about accuracy and about the perceived welfare of the individual it advises. The AI then has an incentive to tilt advice toward what the individual would like to believe, altering both the political content and the informativeness of its messages. Sophisticated individuals anticipate this distortion and filter out its predictable political component, yet still learn less because the AI makes its messages less responsive to the state. Individuals who underestimate the incentive instead mistake political accommodation for information, allowing political preferences to distort factual beliefs and generate polarization and radicalization. The model also shows that better-informed individuals receive more informative and less politically tilted advice, while greater sophistication can improve interpretation yet worsen communication itself. Contrary to the familiar echo-chamber intuition, political distortion is mitigated when political preferences and prior beliefs coincide and is most consequential when they diverge. We show how independently varying individuals’ stated preferences and prior beliefs can recover the AI’s responsiveness to the state even when the underlying state cannot be manipulated…(More)”.
Article by Miriam Waldvogel: “Americans ask AI chatbots about almost everything, from health to relationships to big existential questions. While those conversations might feel enlightening or intimate, they are not always one thing: private.
A Washington Post review of public records and local news stories found a dozen instances of chatbot transcripts being cited in the public record in court cases over the past two years. In many cases, the transcripts were unearthed from users’ devices when searched by a police officer or an opposing party during the evidence-gathering stage of a civil case. In a handful of instances, AI companies have reported disturbing content on their platforms to law enforcement.
“Unless you are having a chat with a service that has a temporary chat or, basically, an incognito version … [and] you’re also having it within a browser that’s not tracking you, the answer is no. You can’t be sure that it’ll be totally private,” said Jen King, a privacy researcher at the Stanford Institute for Human-Centered Artificial Intelligence…(More)”.
Article by Nick Penzenstadler: “Laura Berlin watched images of immigration agents rounding up people and flying them to a Latin American prison without due process – and she knew she had to do something.
“That situation really haunted me,” she said.
She asked herself: “Who owned these chartered airliners that were flying the men to a Salvadoran prison? Who owned these detention centers where men were being held along the way?” In short, what companies were taking money from the government to help deport people? Berlin needed to know. Using her background in nonprofit communications, she built an interactive Google Map titled, “Who is Profiting from ICE?” It plots the contractors across the country who work with Immigration and Customs Enforcement to carry out the Trump administration’s deportation policies.
Activists found her online database and have used it to organize hundreds of protests across the country against these companies, shaming them into cutting ties with ICE. In one case, an East Coast bank broke with two prominent detention center contractors after account holders – including Jersey City, New Jersey – withdrew more than $350 million. The U.S. government found her website, too – and labeled it a tool for domestic terrorism, according to a secret intelligence bulletin obtained by USA TODAY. It’s the latest example of the Trump administration cracking down on dissent it says could veer into political violence…(More)”.