Stefaan Verhulst
Essay by Dennis M. Hogan: “Imagine a large online lecture class taught by a tenured professor with the assistance of AI tutors. The professor writes and delivers her lectures once; they are infinitely re-playable as long as the course content does not change. Meanwhile, AI agents can design assessments, deliver course material beyond lectures, hold students’ hands through the completion of the assignments, and even evaluate students’ work, eliminating the need for human grading. The professor can simply review the AI’s work and enter the grades, though even this is essentially a formality—there’s nothing stopping the AI agent from entering the grades with the registrar directly. What I have just described is a class that costs almost nothing in labor, especially after the professor recorded the lectures in a previous semester. A really ambitious university might assign a single grad student or teaching assistant to the class just to oversee the automated processes, but even this isn’t strictly necessary. The machinery runs on its own.
If you, like me, are attached to the old model of education, which presupposes that teachers, in delivering instruction, enter into personal relationships with students, you might find this scenario depressing. After all, if no one is really teaching, is anyone really learning? If, on the other hand, you are a college administrator desperately trying to align your instructional obligations to your budgetary limitations, you might find such a model enticing. As a student, you might find it alienating, but then again, if you are already used to Zoom school and distance learning, it might not feel all that different. And perhaps the course doesn’t matter to you all that much. You’re just trying to get a requirement or a prerequisite out of the way. This class is not going to change your life.
Much of the discourse around generative AI in education revolves, fairly or unfairly, around the ways that students can use the technology to do their work for them. AI is frequently connected to what commentators have called a “literacy crisis”—a longstanding decline in Americans’ levels of reading fluency affecting adults as well as children. While some early childhood educators and policy experts are optimistic about the potential for AI tools to improve childhood reading acquisition alongside traditional classroom instruction and reading with adults at home, many college instructors have been more skeptical, suggesting that students who rely on AI tools lack the motivation to absorb written texts and do not build the skills to understand them effectively. But if you listen to its boosters in academia and industry, AI is much more than a cognitive shortcut or a text-generating machine: It is a technology that is rapidly threatening to transform the entire economy, as companies make massive bets on its potential to increase productivity and even solve previously insoluble problems such as cancer and climate change. It could even amount to a new industrial revolution. So far, the promises of AI have been slow to materialize: Although the technology offers applications for tasks like coding, there remains an abiding sense of confusion about whether—and how—to integrate AI products into other lines of work. In fact, among the general public, AI skepticism abounds. This probably has something to do with the fact that AI boosters tell us, repeatedly, that AI is going to put many of us out of work…(More)”.
Chapter by Melisa Ross & Azucena Morán: “Democratic innovations allow for the direct involvement of lay citizens, for instance, in climate governance. Latin America, where these approaches have multiplied, offers a compelling context to examine to what extent they help face the interrelated challenges that characterise the Anthropocene, and the resulting planetary condition. We discuss three types of democratic innovations, based on intermediation, deliberation, and mobilisation. They aim to manage the conditions for life, seek the consent of affected communities for the continuation of the planetary condition, or counter its causes and effects. We argue that the limits and ambiguities of these innovations are better understood through historically grounded challenges to the concept of the Anthropocene. In particular, the Plantationocene foregrounds colonial and extractive histories that constrain political action. Democratic innovations alone cannot undo systemic trajectories of exploitation and inequality; historicised and politically anchored lenses such as the Plantationocene better shed light on how they also operate as counter-hegemonic struggles for justice, sovereignty, and habitability…(More)“.
Open Access Book edited by Sahana Udupa and Peter Hervik: “…provides a groundbreaking collection of anthropological research on artificial intelligence (AI), examining how anthropology can help to comprehend and critique its development. Evaluating the limits, hopes and fears of AI, leading experts explore its influence as a sociocultural phenomenon rather than a discrete technological system.
Through an ethnographic lens, the Handbook analyzes human–machine entanglements as they have emerged in AI companions, healthcare, automated policing, warfare, conversational chatbots and fact-checking. Chapters assume theoretical perspectives to scrutinize AI power and how AI evolves on the ground in ethnographic locations and case studies from across the globe. Assessing the current state of AI, the collection examines its implications for the future, in areas ranging from climate change to politics…(More)”.
Report by the WEF: “As AI, clean energy, advanced manufacturing and infrastructure renewal accelerate, there is strong demand for the practical, hands-on skills needed to build, operate, maintain, repair and produce the goods and systems that economies depend on.
This third instalment of the New Economy Skills series examines how these maker skills are becoming a strategic advantage for economies and businesses. Drawing on unique data and insights, the report analyses global supply and demand trends. It finds that the maker skills pipeline is not yet prepared for the scale, pace or complexity of current and future demand. The report outlines how to address three key challenges: adapting to changing skill requirements, attracting more people into maker careers, and ensuring that maker skills are easier to assess, recognize and credential…(More)”.
Paper by Marco Marini, Jim Tebrake, and Andinet Woldemichael: “This paper presents a methodology for downscaling official national and subnational macroeconomic data into high-resolution grids using spatial machine learning techniques. Traditional macroeconomic data are highly aggregated and obscure the spatial distributions needed to understand and quantify local economic activity, impacts of physical risks, and infrastructure gaps. Our hierarchical approach integrates official macroeconomic accounts with Earth observation predictors, such as nighttime lights, built-up areas, land cover, and gridded population, to deliver high-resolution, gridded estimates that remain fully consistent with official subnational and national totals. The empirical application focuses on constructing experimental gridded GDP by ten-sector industry for Canada and the United States. The methodology complements and enhances official statistics by adding spatial granularity through open geospatial datasets and can be extended to generate gridded capital stock estimates…(More)”.
Initiative by the University of Chicago: “Papers have been the de facto unit of knowledge in science. But science is not static and a paper is not meant to be the final destination, especially in empirical research. A paper may propose a novel way to look at the world and offer a snapshot of what it finds. That snapshot is far from the whole story.
The world keeps changing after a paper is published. New data, new models, and new methods give us a chance to return to its questions: does the pattern persist, does the effect size remain the same, and what has changed? As AI agents become more capable, we have an opportunity to make revisiting important work a routine part of science.
Today, we are introducing Living Science, a project to keep influential research alive by revisiting its findings as new data, models, and methods become available. We are starting with economics, focusing on studies whose data sources are regularly updated. The goal is to not assess whether the paper was correct at the time of publishing but to provide resources and venues for continued discussions of influential research.
We use SAI’s replication agent to attempt to reproduce each paper’s key findings, documenting what matches and what does not, before extending the analysis to newer data where possible. The AI agent will first look for public replication packages if available and then identify relevant data sources for extending the analysis. As a result, we may not use exactly the same dataset and setup. The exercise is closer to writing a follow-up paper than strict replication. We sanity-check the results and reach out to the original authors for feedback. We also invite the community to identify issues and post comments, helping us improve the analyses over time…(More)”.
Paper by Valerie Sticher et al: “With armed conflicts at a historic high and attacks on civilians rising, understanding the evolving nature of conflict is a critical research priority. The current paradigm in conflict research relies heavily on text-based data, using fatalities as the primary—and often sole—proxy for violence intensity. Although these data have expanded our ability to study armed conflict, they exhibit inherent limitations due to uneven human reporting. War damage assessments based on satellite data offer a complementary perspective. Satellite-derived data have their own limitations, but these arise from different mechanisms, creating distinct, complementary strengths that can be leveraged through data integration. Here we propose three concrete approaches to integration: improvement, enrichment and fusion. Each bridges a different gap in the underlying data sources. We use case studies from Ukraine and Myanmar to illustrate how integration can be implemented in practice and the novel analytical insights that emerge. Prioritizing data integration enables a paradigm shift away from fatality-centric research towards a broader spectrum of violence, revealing the complexity of conflict dynamics…(More)”
Article by Jacob Taylor, Scott E. Page, Kershlin Krishna, Adam Aley, Vivek Ramakrishnan, Sarah Mills, Joshua Becker, Eirini Malliaraki, Fahd Husain, and Sanjay Purohit: “As the artificial intelligence (AI) boom brings large data center proposals to small and rural communities across the United States and beyond, the most resounding story to emerge is one of mismatched agency. In journalist Jasmine Sun’s words, “big companies descend on a small town and run roughshod over small-d democracy.”
Many people do not want data centers in their communities. And for good reasons: the costs are material, if still not fully understood, spanning noise and visual pollution, land-use change, and rising electricity demand with knock-on effects for water withdrawals and utility affordability. As with most large-scale developments, data centers also offer economic upsides—a surge of construction jobs, considerable tax revenue, and improvements in infrastructure. These can be significant but are not always guaranteed.
Given the scale and impact of data centers, people should have a voice. However, decisionmaking about data center siting is often fragmented, noninclusive, and based on criteria other than their impact on communities. In many parts of the country, states set tax exemptions and approve utility contracts, while local officials, often working part-time, negotiate one siting approval at a time, up against experienced corporate counsel imposing time pressure and confidentiality agreements. Local citizens are rarely directly involved in these processes and, where confidentiality provisions apply, may learn the terms of a proposed deal only after negotiations are well advanced. The result is that residents feel sidelined, fueling skepticism as to whether data center proponents are sharing all relevant information and whether public officials are acting in the community’s best interests. Because data center developments are closely associated with the rapid diffusion of generative AI in the U.S. and elsewhere, uncertainty about the projects themselves can also become entangled with broader concerns about AI’s economic and social impacts, as well as its existential risks.
Known remedies for these challenges exist—namely, community-led processes that surface varied priorities, align on a common vision, and help turn it into action. Historically, such processes have proven costly and complex, requiring time to build the trusting relationships on which collaboration depends—time that can be particularly scarce when communities are responding to fast-moving development proposals. Deliberative processes become more difficult to run when issues are emotionally and politically charged, as is increasingly the case with data center developments in the U.S…(More)”.
Essay by Alondra Nelson: “…argues that while generative artificial intelligence (AI) can be described as a platform, that description is analytically insufficient for understanding where power operates and how it displaces democratic accountability. The platform concept directs attention to the interface—application programming interfaces (APIs), app stores, and developer ecosystems—while the decisive conditions of generative AI lie upstream in compute supply chains, mineral extraction, semiconductor chokepoints, energy and water infrastructures, immigration flows, and corporate-state arrangements that concentrate control. To name this formation, I introduce the concept of structured absence: the organized diminution of democratic institutional capacity in which governance persists while public authorization disappears. Generative AI is not ungoverned; it is extensively governed through export controls, industrial policy, labor regimes, and private infrastructures that remain largely inaccessible to democratic publics. I argue that “predatory inclusion” and authorization are central analytic terms for understanding this shift: communities are incorporated into AI systems on unequal and opaque terms, while the standing to deliberate over those systems is withheld. The essay concludes by calling for democratic infrastructures capable of authorizing technological change…(More)”.
Paper by Donald Moynihan: “The second Trump administration saw the Department of Government Efficiency (DOGE), a group of outsider technologists given extraordinary power to pursue a massive downsizing of the federal government. They also displaced an alternative approach: the civic tech movement. Civic tech for government applies technology, data, and human-centered design to improve policy implementation, fueled by prosocial motivation, promising to increase state capacity in a way that centers attention on public service clients. This article explains the origins of civic tech for government in the US setting, its core values, and evolution. The movement grew in prominence and influence from 2014 to 2024, making steady inroads into government but never gaining sufficient power to implement profound change. The conflicting origins, philosophy, and power between civic tech and DOGE offer contrasting visions for how tech can reshape the American administrative state…(More)”.