Stefaan Verhulst

Article by Mira Mohsini & Andres Lopez: “When the Coalition of Communities of Color (CCC) began a multi-year collaboration with the Oregon Health Authority (OHA), they worked together to modernize a critical public health information source: the Oregon Student Health Survey. This survey, disseminated annually across Oregon, was designed to track health trends and inform policy decisions affecting thousands of young people and families.

But there was a problem. Year after year, this survey illuminated inequities, showing, for example, that students of color experienced higher rates of bullying or mental health challenges, without providing any insight into why these inequities existed, how they were experienced, or what communities wanted done about them. The data revealed gaps but offered no pathways to close them.

Working alongside other culturally specific organizations within their coalition and researchers of color in their region, CCC set out to demonstrate what better data could look like for the Oregon Student Health Survey. They worked with high school teachers who had deep relationships with students and met with students to understand what kinds of questions mattered most to them. Simple and straightforward questions like “How are you doing?” and “What supports do you need?” revealed issues that the state’s standardized surveys had completely missed. The process generated rich, contextual data showing not just that systems were failing, but how they were failing and how students desired their needs to be met. The process also demonstrated that working with people with lived experiences of the issues being researched generated better questions and, therefore, better data about these issues.

And the improvements resulting from better data were tangible. OHA created a Youth Data Council, involving young people directly in designing aspects of the next version of the Student Health Survey. CCC documented the survey modernization process in a detailed community brief. For the first time ever, the Oregon Student Health Survey included three open-ended questions, yielding over 4,000 qualitative responses. OHA published a groundbreaking analysis of what students actually wanted to say when given the chance…(More)”

Article by Thijs van de Graaf: “Artificial intelligence is often cast as intangible, a technology that lives in the cloud and thinks in code. The reality is more grounded. Behind every chatbot or image generator lie servers that draw electricity, cooling systems that consume water, chips that rely on fragile supply chains, and minerals dug from the earth.

That physical backbone is rapidly expanding. Data centers are multiplying in number and in size. The largest ones, “hyperscale” centers, have power needs in the tens of megawatts, at the scale of a small city. Amazon, Microsoft, Google, and Meta already run hundreds worldwide, but the next wave is far larger, with projects at gigawatt scale. In Abu Dhabi, OpenAI and its partners are planning a 5-gigawatt campus, matching the output of five nuclear reactors and sprawling across 10 square miles.

Economists debate when, if ever, these vast investments will pay off in productivity gains. Even so, governments are treating AI as the new frontier of industrial policy, with initiatives on a scale once reserved for aerospace or nuclear power. The United Arab Emirates appointed the world’s first minister for artificial intelligence in 2017. France has pledged more than €100 billion in AI spending. And in the two countries at the forefront of AI, the race is increasingly geopolitical: The United States has wielded export controls on advanced chips, while China has responded with curbs on sales of key minerals.

The contest in algorithms is just as much a competition for energy, land, water, semiconductors, and minerals. Supplies of electricity and chips will determine how fast the AI revolution moves and which countries and companies will control it…(More)”.

Article by Jacob Taylor and Scott E. Page: “…Generative artificial intelligence (AI) does not transport bodies, but it is already starting to disrupt the physics of collective intelligence: How ideas, drafts, data, and perspectives move between people, how much information groups can process, and how quickly they can move from vague hunch to concrete product.

These shifts are thrilling and terrifying. It now feels easy to build thousands of new tools and workflows. Some will increase our capacity to solve problems. Some could transform our public spaces to be more inclusive and less polarizing. Some could also quietly hollow out the cultures, relationships, and institutions upon which our ability to solve problems together depends.

The challenge—and opportunity—for scientists and practitioners is to start testing how AI can advance collective intelligence in real policy domains, and how these mechanisms can be turned into new muscles and immune systems for shared problem-solving…(More)”.

Article by Shana Lynch: “…After years of fast expansion and billion-dollar bets, 2026 may mark the moment artificial intelligence confronts its actual utility. In their predictions for the next year, Stanford faculty across computer science, medicine, law, and economics converge on a striking theme: The era of AI evangelism is giving way to an era of AI evaluation. Whether it’s standardized benchmarks for legal reasoning, real-time dashboards tracking labor displacement, or clinical frameworks for vetting the flood of medical AI startups, the coming year demands rigor over hype. The question is no longer “Can AI do this?” but “How well, at what cost, and for whom?”

Learn more about what Stanford HAI faculty expect in the new year…As the buzz around the use of GenAI builds, the creators of the technologies will get frustrated with the long decision cycles at health systems and begin going directly to the user in the form of applications that are made available for “free” to end users. Consider, for example, efforts such as literature summaries by OpenEvidence and on-demand answers to clinical questions by AtroposHealth. 

On the technology side, we will see a rise in generative transformers that have the potential to forecast diagnoses, treatment response, or disease progression without needing any task-specific labels.

Given this rise in available solutions, the need for patients to know the basis on which AI “help” is being provided will become crucial (see my prior commentary on this). The ability for researchers to keep up with technology developments via good benchmarking will be stretched thin, even if it is widely recognized to be important. And we will see a rise in solutions that empower patients to have agency in their own care (e.g., this example involving cancer treatment)…(More)”.

Paper by Katharina Fellnhofer, Emilia Vähämaa & Margarita Angelidou: “Trust serves both as a social signal and as an alternative governance mechanism, enhancing confidence in collective action and institutional commitment to the public good. This study investigates how trust—particularly in regional organizations—influences citizen engagement in policymaking processes. Drawing on survey data from 7729 respondents across four European regions, via our Bayesian linear mixed-effect model, we find that higher levels of trust in regional organizations and perceived individual’s trust is significantly associated with higher citizen demand for engagement in policy development. However, a notable gender disparity emerges: while women report higher levels of trust in regional organizations, this does not translate into a greater demand for engagement. This finding underscores the need for more inclusive and equity-oriented engagement strategies that address gendered differences in political efficacy and perceived responsiveness. Our results have practical implications for participatory governance, particularly in the context of addressing complex urban sustainability challenges…(More)”. (See also: Making Civic Trust Less Abstract: A Framework for Measuring Trust Within Cities).

Article by Ellie McDonald and Lea Kaspar: “As the dust settles on the World Summit on the Information Society (WSIS) 20-year Review, attention is turning to what the final outcome document (adopted by consensus on 17 December) ultimately delivers. For much of the review, discussions were pragmatic and forward-looking, reflecting a shared interest in maintaining the relevance of the WSIS framework amid a rapidly evolving digital policy landscape. As negotiations moved into their final phase, focus narrowed to a smaller set of long-standing questions, shaping the contours of the text that was agreed.

The outcome document does not seek to resolve all of the issues raised during the review. Rather, it reaffirms core principles, clarifies institutional roles, and sets out expectations for implementation that will now need to be tested in practice.

As negotiations concluded, GPD intervened during the WSIS+20 high-level event this week, emphasising that legitimacy in digital governance is not secured by consensus alone, but depends on sustained participation, human rights anchoring, and accountability as frameworks move into implementation. Read the full intervention here…(More)“.

Article by Aimee Levitt: “This past March, when Google began rolling out its AI Mode search capability, it began offering AI-generated recipes. The recipes were not all that intelligent. The AI had taken elements of similar recipes from multiple creators and Frankensteined them into something barely recognizable. In one memorable case, the Google AI failed to distinguish comments on a Reddit thread from legitimate recipe sites and advised users to cook with non-toxic glue.

Over the past few years, bloggers who have not secured their sites behind a paywall have seen their carefully developed and tested recipes show up, often without attribution and in a bastardized form, in ChatGPT replies. They have seen dumbed-down versions of their recipes in AI-assembled cookbooks available for digital downloads on Etsy or on AI-built websites that bear a superficial resemblance to an old-school human-written blog. Their photos and videos, meanwhile, are repurposed in Facebook posts and Pinterest pins that link back to this digital slop.

Recipe writers have no legal recourse because recipes generally are not copyrightable. Although copyright protects published or recorded work, they do not cover sets of instructions (although it can apply to the particular wording of those instructions)…(More)”.

Article by James Grimmelmann: “…In response to these tussles, various groups have started trying to create new versions of robots.txt for the AI age. Many of these proposals focus on making REP more granular. Instead of a just binary decision—allow or disallow access—they add mechanisms for websites to place conditions on the usage of the contents scraped from it. This is not the first such attempt—a group of publishers proposed a system called the Automated Content Access Protocol in 2006 that was never widely adopted—but these new ones have more industry support and momentum.

Cloudflare’s Content Signals Policy (CSL) extends robots.txt with new syntax to differentiate using scraped content for search engines, AI model training, and AI inference. A group of publishers and content platforms has backed a more complicated set of extensions called Really Simple Licensing (RSL) that also includes restrictions on allowed users (for example, personal versus commercial versus educational) and countries or regions (for example, the U.S. but not the EU). And Creative Commons (disclosure: I am a member of its Board of Directors) is exploring a set of “preference signals” that would allow reuse of scraped content under certain conditions (for example, that any AI-generated outputs provide appropriate attribution of the source of data).

At the same time, some of these same groups are trying to extend REP into something more ambitious: a framework for websites and scrapers to negotiate payment and content licensing terms. Cloudflare is experimenting with using the HTTP response code 402 PAYMENT REQUIRED to direct scrapers into a “pay per crawl” system. RSL, for its part, includes detailed provisions for publishers to specify commercial licensing terms; for example, they might require scrapers to pay a specified fee per AI output made based on the content.

Going even further, other extensions to RSL include protocols for crawlers to authenticate themselves, and for sites to provide trusted crawlers with access to encrypted content. This is a full-fledged copyright licensing scheme built on the foundation—or perhaps on the ruins—of REP.

Preserving the Best of the Open Web

CSP, RSL, and similar proposals are a meaningful improvement on the ongoing struggle between websites and AI companies. They could greatly reduce the ongoing technical burdens of rampant scraping, and they could resolve many disputes through licensing rather than litigation. A future where AI companies and authors agree on payment for training data is better than a future where the AI companies just grab everything they can and the authors respond only by suing.

But at the same time, RSL similar proposals move away from something beautiful about REP: its commitment to the open web. The world of robots.txt was one where it was simply expected, as a matter of course, that people would put content on webpages and share it freely with the world. The legal system protected websites against egregious abuses—like denial-of-service attacks, or wholesale piracy—but it treated ordinary scraping as mostly harmless…(More)”

Article by Jeff Jarvis: “News organizations will face an AI reckoning in 2026 and a choice: They can keep blocking AI crawlers, suing AI companies, and lobbying for protectionist AI legislation — all the while making themselves invisible to the publics they serve on the next medium that matters — or they can figure out how to play along.

Unsurprisingly, I hold a number of likely unpopular opinions on the matter:

1. Journalists must address their civic, professional, even moral obligation to provide news, reporting, and information via AI. For — like it or not — AI is where more and more people will go for information. It is clear that competitors for attention — marketing and misinformation — are rushing to be included in the training and output of large language models. This study finds that “reputable sites forbid an average of 15.5 AI user agents, while misinformation sites prohibit fewer than one.” By blocking AI, journalism is abdicating control of society’s information ecosystem to pitchmen and propagandists.
2. AI no longer needs news. Major models are already trained and in the future will be trained with synthetic data. Next frontiers in AI development — see, for example, the work of Yann LeCun — will be built on world models and experience, not text and content.
3. Anyway, training models is fair use and transformative. This debate will not be fully adjudicated for some time, but the Anthropic decision makes it clear that media’s copyright fight against training is a tenuous strategy. Note well that the used books Anthropic legitimately acquired yielded no payment to authors or publishers, and if Anthropic had only bought one copy of each title in the purloined databases, it would not have been found liable and authors would have netted the royalties on just one book each.
4. AI is the new means of discovery online. I had a conversation with a news executive recently who, in one breath, boasted of cutting off all the AI bots save one (Google’s), and in the next asked how his sites will be discovered online. The answer: AI. Rich Skrenta, executive director of the Common Crawl Foundation, writes that if media brands block crawlers, AI models will not know to search for them, quote them, or link to them when users ask relevant questions. He advises publishers to replace SEO with AIO: optimization for AI. Ah, but you say, AI doesn’t link. No. This study compared the links in AI against search and found that ChatGPT displayed “a systemic and overwhelming bias towards Earned media (third-party, authoritative sources) over Brand owned and Social content, a stark contrast to Google’s more balanced mix.” The links are there. Whether users click on them is, as ever, another question. But if your links aren’t there, no one will click anyway…(More)”.

Book Review by Charles Carman: “One day, Mrs. Pengelley came to London seeking the assistance of Hercule Poirot, Agatha Christie’s Belgian detective with the mustache, whose “little grey cells” assist him in solving mysteries. With a troubled look, she tells him that she fears she is being slowly poisoned. The doctor doesn’t see anything much the matter, she says. He attributes the stomach trouble to gastritis. She even sometimes improves, but strangely this happens during the absence of someone in her life, confirming in her a certain suspicion.

After listening to her tale with great interest, Poirot agrees to take up the case. He sends the lady back and plans to catch a train the following day to begin his investigation. Discussing the matter with his close friend, Captain Hastings, Poirot admits the case is especially interesting, even though “it has positively no new features,” because “if I mistake not, we have here a very poignant human drama.”

When Poirot arrives the next day, he discovers that the lady has been murdered after unwittingly taking the final dose of poison. Having found the case intriguing enough to look into it, Poirot chastises himself, a “criminal imbecile,” for not having taken her story more seriously. “May the good God forgive me,” he declares, “but I never believed anything would happen at all. Her story seemed to me artificial.” Had he been convinced enough to return with her right away, he might have saved her. All that remains for him now is to catch the murderer.

“The Cornish Mystery” occurred to me while reading Paul Kingsnorth’s new collection of essays, Against the Machine: On the Unmaking of Humanity. In the story he weaves, a sinister force has been lurking for some time within our civilization, especially in the West. His suspicion falls upon something to do with science, technology, and how we misapprehend the world. It has been slowly sapping away at our life, creating problems that have been diagnosed as this or that malady and treated with such and such a remedy. Sometimes we feel better. And yet, we sense we are being dehumanized, unmade, that something essential is being destroyed piece by piece. Such a process is hard to pin down. This is the genius of murder by slow poisoning: it leads to doubt and misattribution. There is little ambiguity about a gunshot to the heart. Yet when killing dose by dose, one easily mistakes murderous intent with the body’s frailty, a lingering affliction, or incidental complications: murder disguised as natural causes…(More)”.