Stefaan Verhulst
Essay by Stefaan Verhulst: “…The need for a science of questions is all the more acute, and growing more acute, because of the rapid advance of generative AI (as emphasized in previous writing: ChatGPT reminds us why good questions matter). AI has brought to the fore the practice of prompting, both as a marketable skill and as a way of eliciting and organizing information by asking generative models questions about the world. But prompting is, in important respects, almost the opposite of genuine questioning. As we argue below, prompting typically begins from a preconceived sense of what a good answer looks like, whereas questioning begins in genuine not-knowing. As machines make answers ever cheaper and more abundant, the capacity that grows scarcer and more valuable is the one prompting does not train (and perhaps actively degrades): the ability to formulate the questions worth asking in the first place. That is the distinction this essay draws out, and it is why the case for a science of questions has become urgent in the age of AI, rather than merely an interesting theoretical exercise.
What a Prompt Is
A prompt is defined by its relationship to a system (or person) that will complete it. It is primarily instrumental: the prompter has some model, explicit or intuited, of how the system responds to inputs, and shapes the input to produce a desired class of outputs. Good prompting is a form of engineering, and its virtues are the virtues of engineering: precision, reproducibility, efficiency, minimal ambiguity, and a clear line between intent and result.
Most prompting, most of the time, also begins from a position of relative epistemic confidence. The prompter may not be able to produce the output themselves, but they can usually recognize a good result when they see it — that’s what makes iterative refinement (prompt, assess, revise) possible. In this sense, a typical prompt is closer to a query than to a question: a request for something whose contours, even if not exact form, the asker already knows.
This is not intended as criticism, and these observations are not categorical or absolute. Prompting can at times be exploratory, and some of the most interesting prompting deliberately courts surprise. But in general, prompting describes a particular and historically recent practice, one characterized by a relationship between an operator and an instrument. The mistake is not in the practice itself. It is in letting that relationship stand in for the far larger territory of human questioning, most of which has nothing to do with directing any system at all…(More)”.
Article by Ian Bogost: “AI-assisted software development could have liberated programmers. Instead, it has become the latest way for Big Tech to confine us…My family and I enjoy the occasional meal of delicious slow-cooked meat, but every time we visit a barbecue joint, we can’t remember whether we like the beans and slaw on offer. Some beans are sweet; others are spicy. Some slaw is vinegar based; others creamy. We should keep track of this somehow, we always say but then never bother. I should make an app, I used to joke. But that was a ridiculous idea.
Thanks to generative artificial intelligence, it became less ridiculous. By last summer, I had been making small programs with AI for some time. AI’s adeptness at programming was helping me, a busy person with a professional programming background but limited reason to program, work creatively with computing for the first time in ages. I wanted to see how far I could push AI as a solo programmer. The barbecue-sides-preference problem, I reasoned, would serve as a perfect test case: building a fully featured iPhone app—Beanslaw, I called it—with esoteric utility that nobody would have bothered commercializing.
Two days of occasional programming were sufficient to make a professional-grade version of Beanslaw—an act that would have taken weeks of full-time dedication not long ago. The app displayed smoked-meat joints on a searchable map. It implemented sophisticated intelligence to filter out Korean barbecue, Brazilian churrasco, and other (no less delectable!) cuisines that entail barbecue of a different, bean-and-slawless variety. A user can assign assessments of beans (such as vegetarian and soupy) and slaw (including chunky and mustard), and filter the map by desired side-dish traits. I even built an onboarding tutorial. I was ready to ship…(More)”.
Essay by Geoffrey Smith: “…Bibliometricians have a name for papers like Shimomura’s; they call them “sleeping beauties.” A sleeping beauty is published, ignored, and then, sometimes a generation later, kissed awake. The fairy-tale framing is a little twee, but it points at something unsettling about how knowledge grows. We tend to picture the scientific literature as a frontier that moves forward. The sleeping beauties suggest it is closer to an attic, where things of great value lie in boxes that no one has thought to open. And we are now building machines that can open all the boxes at once.
The term comes originally from Anthony van Raan, a professor of quantitative studies of science at Leiden University. In a 2004 paper in Scientometrics, he defined a “Sleeping Beauty in Science” as a publication that goes unnoticed for a long time and then, fairly abruptly, draws a great deal of attention, and he reported what he believed was the first extensive measurement of how often this happens. He scored papers on three criteria: how long they slept, how deeply (the average number of citations during the dormant period), and how intensely they woke (the citations received in the four years after). Using somewhat arbitrary thresholds, he found sleeping beauties at a rate of about 0.01 percent of papers published in a given year (Aghayeva, Works in Progress).
That number made the phenomenon look like a rarity, a handful of mispriced assets in an otherwise efficient market. It did not hold up. In 2015, a team at Indiana University led by Qing Ke and Alessandro Flammini returned to the question with far more data. They introduced a single measure capturing both the intensity of a paper’s recognition and the length of its sleep, and concluded that sleeping beauties are far from exceptional. They noted that earlier estimates of scarcity depended heavily on arbitrary thresholds applied to small or single-discipline datasets. Drawing on 384,649 papers from American Physical Society journals and more than 22 million from Web of Science, they found a wide, continuous range of delayed recognition in every scientific field, and raised the estimated share of sleeping beauties at least 100-fold over van Raan’s (Aghayeva, Works in Progress). Delayed recognition, in other words, is not a curious exception. It is a normal part of how science works…(More)”.
Paper by Vera Lummis, Angela Aristidou and Rob Reich: “Frontier AI labs and nonprofit organizations are increasingly partnering to deploy frontier AI in support of nonprofits’ missions to improve global health, climate, humanitarian, and development outcomes. Individual and institutional funders have invested hundreds of millions of dollars to support nonprofits in deploying AI for good, occasionally in formal partnership with AI labs. However, the rapidly growing AI for Social Good (AI4SG) ecosystem lacks a clear understanding of what these partnerships provide, how they form, and what drives or compromises their success.
Drawing on interviews with 36 individuals across 24 organizations (frontier AI labs, nonprofits, philanthropic funders, and independent experts) and four in-depth case studies, this report offers insights into how nonprofits and frontier AI labs partner to deploy AI to produce social impact and provides recommendations for how partnership and funding arrangements can be structured for success…(More)”.
Paper by Victoria Gorton a and Morgan Currie: “This paper outlines benefit recipients’ views towards the sharing of data that is collected about them when they routinely interact with social security services. Sharing this administrative data between public bodies promises to transform the delivery and evaluation of social security. However, there remains a lack of research into public attitudes, understandings, and aspirations for the sharing of this data, particularly from those whose data would be shared.
Drawing on discussions from deliberative focus groups with 41 individuals, we reflect on the scope for increased data sharing to deliver improvements for recipients. While participants were aware of how their data is currently shared and how increased data sharing might have advantages, they expressed strong distrust in government agencies to manage their data and concerns about being placed in a position of increased vulnerability. We argue individuals’ views towards data sharing cannot be separated from their lived experiences of data systems – their data realities – which are often markedly different from system designers. We conclude by offering considerations against data systems re-introducing paternalistic
policy, unintentionally, via automation – which we term ‘automated paternalism’…(More)”.
Article by Joseph Foti: “In the prior OGP Horizons piece in this series, we looked at what governments actually mean when they talk about tech sovereignty. In the abstract, this is an ability to control technology in their borders and to be relatively autonomous when it comes to using foreign technology.
But, in practice, governments are quite diverse. As a recap, we looked at the legislation and policies being adopted by many “middle powers,” as well as the EU, the US, and China. (Readers are encouraged to skim the summary of all of the findings here.) The findings showed that interventions fell into three categories.
- Cost: Buying software and computing power and spending money to build domestic capacity.
- Exit: Preserving the ability to leave a bad relationship.
- Leverage: Controlling holdings that other jurisdictions need.
Within each of these categories, there are a number of different policies. In Part II of the series, we will go through cost, exit, and leverage. We’ll also look at why and where open government can help ensure that people are getting what they need from the policies, namely autonomy, good value, security, and jobs.
For each of the given policy areas, there is a distinct role for transparency and public oversight. In some cases, especially to those related to land and permitting as well as standards development, there is a particular role for public participation…(More)”.
OECD Report: “Governments are under growing pressure to respond more effectively to rapid technological, economic and societal changes while maintaining stability and fiscal sustainability. A significant transformation of the functioning of governments needs to take place that can take advantage of digital and AI tools, and the opportunity of large cohorts of retiring civil servants to change the culture. Based on the analysis of cutting-edge practices in governments of OECD countries, Government Unstuck provides a coherent set of actions that will help governments be up to the challenges of today…(More)”.
Paper by Dimitrios Kalogeropoulos, Paul Barach, Andrea Downing, Stefaan Verhulst, and Maryam Lustberg: “Healthcare services and data ecosystems remain fragmented, inequitable and misaligned with patient, clinician and public health needs. Three pathways to patient-centred Artificial Intelligence (AI) have emerged to address this: data collaboration, Whole System Approaches and digital inclusion. Each has advanced the field. However, none delivers the continuous, participatory and outcome-grounded learning that equitable AI demands. The populations whose data drives AI learning must also be the populations who benefit from it. This principle has not been structurally embedded in any existing pathway. This narrative review defines a framework for operationalising Adaptive Machine Learning (AML) as a fourth, integrative pathway to patient-centred AI. AML is defined as continuous learning from real-world individual-level data to generate personalised, population-level insights returning direct benefit to the communities who produce them. A narrative review of PubMed and Google Scholar (2018–2025) was conducted across five thematic domains: AI and machine learning in healthcare; real-world data and evidence; patient-centred care and digital health; data governance and AI regulation; and adaptive clinical trial design. Search terms covered healthcare AI applications and adaptive, continuously learning systems, emphasising prediction-oriented rather than static diagnostic approaches. Eligibility was determined through iterative thematic analysis. Eight systemic barriers to patient-centred AI are identified and the three existing pathways critically examined for limitations in addressing bias, validation and equitable implementation. AML is proposed as the mechanism that closes this gap, connecting systems medicine, personalised medicine and precision medicine through continuous, outcome-based learning, identifying three original conceptual contributions as enablers: Integrative Data Governance (IDG), Adaptive Clinical Trial (ACT) designs and Evidence Sandbox Facilities (ESFs). Progress requires globally standardised Healthcare Data Recycling frameworks, adaptive evaluation methodologies and ESFs enabling multi-stakeholder validation. Future research should address federated AML validation, collective consent mechanisms and alignment of regulatory frameworks with international AI governance standards…(More)”.
Paper by Curtis Ventriss and Tina Nabatchi: “Public administration is at the center of the global democratic crisis. Meeting this challenge requires more than managerial and analytic competence—it requires a public philosophy grounded in the first principles of democratic ethos. We offer such a philosophy, drawing on four seminal thinkers whose concepts reflect the conditions public administrators face in times of democratic crisis. Judith Shklar’s liberalism of fear establishes a negative obligation to refuse cruelty and resist its institutionalization. Alasdair MacIntyre’s internal goods of practice create a positive obligation to cultivate virtue and protect the integrity of administrative practice. Isaiah Berlin’s agony of choice obligates administrators to acknowledge irreducible value conflicts and their consequential impact on democratic life. Hannah Arendt’s enlarged mentality provides a framework for thinking across perspectives so that judgment under conflict becomes democratically defensible. Together, these concepts form a public philosophy for public administration with practical significance for administrative judgment, organizational culture, and institutional courage…(More)”.
Article by sheets.works: “Billions of phones run on code looked after by a handful of people. We counted them from the code itself…
People post this comic whenever something breaks. The tower is labelled “all modern digital infrastructure” and it stands on one small block, “a project some random person in Nebraska has been thanklessly maintaining since 2003”.
We wanted to know how close to true it is. We downloaded the full history of 23 pieces of software that phones, browsers and servers depend on, and counted everyone who made ten or more changes to each one between October 2025 and October 2026. Then we listed what is actually installed on an Android phone, an iPhone and a Windows PC…(More)”.