Stefaan Verhulst
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)”.
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)”.