Stefaan Verhulst
Article by Zoe Baird: “The leaders of the major frontier AI developers are sounding the alarm at the speed at which AI is creating potential catastrophic risk, calling for a slowdown and government regulation. But Congress and the president are far from consensus on government’s role. Congress is unprepared and needs to devote serious attention to legislative options. As it does, it should quickly establish a bipartisan public-private AI Commission to define solutions for responding to public alarm and industry warnings.
There is precedent for using a Commission to build rapid consensus in response to a radical change in the threats at hand. The end of the Cold War was one such moment. The terrorist attack of September 11 was another. In each case, the existing machinery of the US government had been built for a world that no longer existed. The moment required a clear-eyed rethinking of government’s role itself, at a speed government rarely manages.
We are in such a moment now. The people building artificial intelligence — the engineers, researchers, and executives who understand the technology best — are the ones sounding the loudest alarms. That is not normal. Industries do not typically lobby for their own regulation or warn that their products could cause catastrophic harm. When this happens, the debate about whether government needs to act should be over. What remains are the hard questions: what should government’s role be, and how does it organize itself to protect the AI future while maintaining America’s lead in AI development?
Certain steps should be taken right away, like requiring embedded independent reviewers and government safety review, but for Congress to frame regulation it should establish an AI Commission and give it six months to make its initial report. This will provide a thoughtful space where points of view can be explored deeply and participants can learn from each other. Its membership should include congressional leaders of committees for technology, commerce, and national security; former government officials; public interest and private sector representatives, including but not limited to AI developers; and independent domain experts. There are four questions Congress should ask…(More)“.
Paper by Gary King and Steven Worthington: “We show that AI-generated article reviews are now better, by every measure we could construct, than reviews written by well-compensated human experts and judged to be of unusually high quality by journal editors and authors. We then discuss how this result may inform journal policy choices, with concerning downsides as well as potential paths to increase publication speed and quality; reduce adversarial interactions; improve the experience of editors, reviewers, authors, and readers; and advance science…(More)”.
Paper by Fengli Xu et al: “Large language models (LLMs) are transforming computational social science. This Review examines how LLMs now serve four distinct research roles: subjects that simulate human behaviour, assistants that automate analysis, collaborators that generate hypotheses and evaluators that audit and improve research quality. We analyse recent advances across sociology, political science, psychology, anthropology, economics and related fields, revealing how LLMs accelerate discovery while creating new methodological paradigms. This transformation arrives as society itself becomes saturated with artificial intelligence agents—making LLMs both our research tools and authentic research subjects. Yet fundamental challenges persist. LLMs struggle with causal inference and raise urgent ethical concerns about privacy and misinformation, and their ability to reproduce social complexity remains uneven and unproven. We identify critical research directions for harnessing LLMs’ potential while addressing their limitations. As artificial intelligence reshapes human society, understanding this transformation requires the very tools driving it—a recursive challenge that will define the future of social science…(More)”.
Article by Emily Bazelon: “Artificial intelligence is alarming many of its human creators by leaping the guardrails designed to contain it. In the last few months, safety researchers have resigned from OpenAI and Anthropic, warning that their former employers could be courting disaster: A.I. that escapes our control irrevocably. The companies are reportedly gaming out scenarios of a catastrophic cyberattack or other event in the next six to 12 months.
Regulating this powerful and evolving technology would pose serious challenges for any Congress and White House. As it is, President Trump undermined the chances for bipartisan regulation by calling predictions of an A.I. catastrophe a “hoax” in September. Yoshua Bengio, a scientist and godfather of A.I., wrote in October, “I do not believe we are on track to see sufficient government regulation in time.”
What should the government do? We asked a panel of experts to assess the risks A.I. presents and the tools of governance that could be adopted to address them.
Alex Bores is a New York legislator who sponsored the bill on A.I. safety that passed in his state last year. He recently helped found a group, Who Decides, that aims to develop A.I. policies with Democrats.
Kate Brennan, a former Google tech worker, leads the policy agenda on A.I. and corporate power at the AI Now Institute, an independent research institute providing expert analysis on A.I. in the public interest.
Tyler Cowen is a professor of economics at George Mason University and runs the podcast “Conversations With Tyler.” He has worked with Anthropic, OpenAI and Mercor on issues related to A.I. and economics.
Daniel Kokotajlo, a former OpenAI researcher, founded and runs the AI Futures Project, which is best known for publishing AI 2027 (a forecast of the current trajectory of A.I. development) and AI 2040: Plan A (a vision for how it should be regulated instead).
Sneha Revanur is the founder and president of Encode, an advocacy organization that works on passing laws to make A.I. safer. She helped organize a July letter, signed by more than 1,000 employees at A.I. companies, that asked to slow down development…(More)”.
Article by Colin Lecher: “The Arkansas-based data broker Acxiom claims it has data on billions of people around the world, including where they live and how they might spend their money. It says it can even deduce their political leanings, weight, and interest in medical procedures.
But what exactly does the company know?
For Tracey Reed, a nonprofit worker in Oregon, it knew enough to compile a 58-page dossier charting the purported history of her income, employment, and education, not to mention her spending, including everything from her online shopping to charitable giving. Although much of the information was incorrect, she said, the company attempted to log where she lived, complete with latitude and longitude coordinates. And it listed dozens of companies to which it had sold data or inferences about her.
“I think it’s pretty gross that people are treated like sources of wealth to be mined,” she said. “And that’s what these data profiles are, like, ‘Here’s a guide for how to squeeze this person and get money out of them.’”…(More)”.
Book by Paul Verhagen: “…Liberal democracies have treated political development as a finished project. Markets would spread freedom, technology would spread markets, and government would become increasingly administrative. That consensus left democratic institutions unprepared for companies that control public infrastructure, information, and increasingly the terms of political decision-making.
This book is an audit of that failure: what was surrendered, who now holds power, and why the political language we inherited can no longer describe the world we live in…(More)”.
The Economist: “…Read the headlines in Western media and it may seem as though a backlash to AI is spreading fast everywhere. But so far, it is not. Mr Bae’s sunny disposition about AI is shared by almost two-thirds of South Koreans, according to a survey that GlobeScan, a polling firm, conducted for The Economist. Outside rich countries this good feeling is even more strongly shared: nearly nine in ten people in Nigeria and Vietnam, for example, said that AI will improve not only their own lives, but the lot of humanity (see chart 1).
The polling across 34 countries was carried out online, which could tilt attitudes in favour of AI. Yet in the same survey sentiment in rich Western countries was much gloomier. In America, Canada and France more people felt negatively than positively about AI’s future impact on humanity. In other Western countries sentiment was net-positive, but far less so than in the poor world. Another recent survey of 37 countries, carried out by Gallup with Microsoft, returned similar results: AI is broadly popular, but less so in rich Western countries.
Why the gap between the West and the rest? Some of the optimism may cool as more people in poor countries come into direct contact with AI or data centres, and see the technology helping or hurting some more than others. Around 9% of working-age Kenyans used AI in the second quarter of 2026, according to a study by Microsoft. Yet 85% told GlobeScan that it would change their lives for the better…(More)”.
Paper by Jess Reia, Jonathan van Geuns and Nicole Bassoff: “AI is becoming pervasive in everyday life, appearing as a visible and constant presence in many aspects of work, education, and personal relationships. It restructures job markets, creates new slop aesthetics, and is forcibly incorporated into almost all digital services with promises of economic growth and efficiency, without any serious reflection on the social and political consequences. But we believe that the situation is not hopeless or settled, no matter how powerful the corporations or systems profiting from AI may appear. The trick is to recognize that democratic levers for change are not given in advance, but must be actively and creatively reimagined. This collection of essays is a survey of where such handles are hiding in the digital age, in places considerably less obvious than a county hearing. In procurement, in a watershed boundary, in a tax treaty almost nobody knows exists, in a benchmark written by users rather than engineers, or in a legal assumption about how technology “works” that is ripe for challenging. None of these were designed as democratic instruments, which is precisely the point. Democracy is presented not as a form of government but a way of living, and as a skill that can be practiced. The authors reflect on an emergent, collective argument about the different meanings and possibilities of “digital democracy from below.” They go beyond the U.S. context to explore distinct but interdependent dynamics of technological politics in places around the world. The collection has five sections: (1) Seeing through the fog; (2) Recognizing the limits of infrastructure; (3) Fighting erasure; (4) Practicing everyday democracy; and (5) Resisting, organizing and re-building. It also offers a roadmap for organizing participatory workshops that can adapted and reused…(More)”.
Paper by Manotar Tampubolon: “The blend of artificial intelligence (AI) and Indigenous Knowledge Systems (IKS) creates a fresh approach to developing technology that is both ethical and inclusive. This research dives into how AI, when paired with Indigenous perspectives, can lead to more comprehensive and culturally aware solutions in tech. Traditional Indigenous knowledge, grounded in strong ethical values and a deep understanding of context, provides insights that are often missed in the AI landscape. However, the commercialization and misuse of Indigenous knowledge through AI raise serious issues, such as the risk of losing cultural heritage, ownership rights, and the reinforcement of harmful stereotypes. This study aims to explore how AI and Indigenous knowledge can come together to foster fair and ethical innovations, tackling the gaps and challenges in today’s tech practices. By utilizing secondary data sources like case studies and theoretical frameworks, the research employs a virtue ethics lens to examine the ethical aspects of this intersection. It emphasizes the necessity for AI systems that are culturally sensitive and that honor Indigenous ways of knowing, ensuring technology benefits a variety of communities. The findings indicate that when AI is developed with Indigenous insights, it can greatly improve ethical decision-making, boost inclusivity, and help eliminate harmful biases found in mainstream AI. Nonetheless, challenges remain, such as securing proper consent, preventing cultural appropriation, and avoiding exploitation. This research highlights the critical need for collaboration between Indigenous communities and AI developers, providing suggestions for building inclusive, equitable, and sustainable tech frameworks…(More)”.
Article by Eric Boodman: “The bug looked like a new species. It had the classic mark of the dead-leaf mantises — a flattened shield, brown and crinkled to mimic forest-floor detritus — but the camouflage was almost too good, as if it were wearing an actual leaf. It was unlike any specimen Kris Anderson had ever seen.
Mr. Anderson has seen plenty of specimens. He has thousands pinned in drawers in his Las Vegas home, legs out, wings spread to reveal a stained-glass geometry of veins.
Every evening after his workday as a director of a counseling center, he spends two and a half hours poring over the 500 or 600 mantis pictures that enthusiasts have uploaded to the citizen science website iNaturalist, where anyone can submit photos of wildlife to help map biodiversity. To Mr. Anderson, most are junk — blurry shots of common species. But every so often there is something worth writing up: a mantis never before documented on Aruba, or a rare behavior, like three females seemingly eating each other.
For a few seconds, the mantis he saw on June 17 seemed like a taxonomic jackpot. Then he realized he’d been duped. This mantis was fake, a product of artificial intelligence…(More)”.