Stefaan Verhulst
Article by Richard A. Greenwald: “The “Two Cultures” lecture has aged into one of those texts that everyone mentions and nobody reads. The broad strokes are familiar: in 1959, C. P. Snow, an English chemist and novelist, stood before a Cambridge audience and announced that Western intellectual life had fractured into two distinct camps — scientists and humanists — who had ceased to communicate with one another. The scientists, in Snow’s telling, were optimistic modernizers with the “future in their bones”; the literary men were nostalgic reactionaries who couldn’t describe the Second Law of Thermodynamics and rather wished the future would not arrive. Snow found this tragic. Everyone nodded gravely. The essay became a perennial touchstone for handwringing about disciplinary silos and the need for well-rounded education, eventually landing on the Times Literary Supplement’s list of the hundred most influential books since World War II.
Here’s what’s funny about the whole thing: Snow was worried about the wrong problem. He fretted that humanists and scientists couldn’t communicate across their divide. What he couldn’t have anticipated is that six decades later, neither group would be setting the terms of public discourse at all.
Over the years, various observers have argued that the divide no longer exists, because the scientists won. In 1995, John Brockman claimed in his book The Third Culture that scientists were becoming the new public intellectuals, communicating directly with general audiences while literary intellectuals retreated into irrelevance.
He was half-right. Today a third culture does dominate public discourse, and it is technocratic. But it is not the culture of tweed-jacketed classicists nor of white-coated researchers. Both of those have been absorbed — digested, really — into a culture that didn’t exist when Snow was lecturing: the culture of venture capital, of pitch decks and product–market fit, of founders who look at the accumulated wisdom of human civilization and see a legacy codebase overdue for refactoring…(More)”.
Paper by Lin Kyi, Paul Gölz, Robin Berjon, and Asia J. Biega: “Obtaining meaningful and informed consent from users is essential for ensuring autonomy and control over one’s data. Notice and consent, the standard for collecting consent, has been criticized. While other individualized solutions have been proposed, this paper argues that a collective approach to consent is worth exploring. First, individual consent is not always feasible to collect for all data collection scenarios. Second, harms resulting from data processing are often communal in nature, given the interconnected nature of some data. Finally, ensuring truly informed consent for every individual has proven impractical.
We propose collective consent, operationalized through consent assemblies, as one alternative framework. We establish collective consent’s theoretical foundations and use speculative design to envision consent assemblies leveraging deliberative mini-publics. We present two vignettes: i) replacing notice and consent, and ii) collecting consent for GenAI model training. Our paper employs future backcasting to identify the requirements for realizing collective consent and explores its potential applications in contexts where individual consent is infeasible…(More)”.
Article by Ruchir Sharma: “The GDP report later this week is likely to show that economic growth in the US once again topped 2 per cent. But you wouldn’t have seen this coming in measures of the popular mood. Consumers keep spending, even as the gap between what they spend and the pessimism they express in surveys has never been higher.
Currently the two main consumer surveys, from the Conference Board and the University of Michigan, are at lows typical of a recession, not a steady expansion. In fact, lower confidence readings have been registered only twice over the last three decades, including during the global financial crisis of 2008.
The same disconnect is visible in key measures of business confidence. Several times since the pandemic, the ISM surveys of manufacturers and service firms have signalled a recession, which never came. The newer, broader business surveys from S&P have been closer to the mark, but despite a recent rebound, all these readings remain at levels weaker than the actual growth picture.
The monthly survey releases still get a lot of attention in the media and on Wall Street, which is a bit odd since the results are essentially broken. Falling response rates distort their findings. Social media seems to breed discontent regardless of how fast the economy is growing. In a polarised environment, partisan voters always think conditions are dismal when a rival party is in power. For these reasons and more, recent studies have found the reliability of major surveys falling not only in the US but in the Eurozone and UK as well.
Perhaps most significantly, surveys are naturally skewed by rising inequality. Unlike aggregate GDP growth figures, surveys give equal weight to every respondent. So they are never going to capture or foretell the full extent of GDP growth, when growth is increasingly dependent on the spending of a few. And that is what is happening now. In the US, the richest 10 per cent account for half of consumer spending, up from a third three decades ago. It should not be surprising the majority sounds pessimistic…(More)”.
Blog by Stefaan Verhulst: “For two decades, the open data community has worked to articulate what it means to make data fit for use, converging on the now-canonical FAIR principles: Findable, Accessible, Interoperable, and Reusable. As we argue in Moving Toward the FAIR-R Principles, this framework, though foundational, is no longer sufficient in an era of artificial intelligence. It must be extended to include a fifth commitment: Ready for AI.
The conversation over data readiness for the AI age is not confined to the public-interest domain. Parallel discussions have unfolded in the corporate sector, where the imperatives of AI readiness have been confronted at scale, and it is worth asking what the open data movement might learn from them.
McKinsey’s recent article, AI Data Readiness: The Key to Scaling Impact, is instructive in this regard. On its surface, the article reads as a memo to chief data officers diagnosing why corporate AI pilots so often stall before reaching production. It also offers some valuable lessons and principles for data stewards operating in the public interest, and who may be considering how to apply FAIR-R principles in practice.
In what follows, we treat this private-sector-focused article as a source of transferable knowledge while still remaining attentive to its limits. The caveat about limits is consequential, and we return to it below: in general, while enterprises optimize primarily for reliable outputs and cost avoidance, public-interest stewardship must also account for equity, openness, and public accountability. Some lessons therefore transfer cleanly, while others may require translation (or simply not apply at all)…(More)”.
Paper by Adam Zable, and Stefaan Verhulst: “This paper reports findings from a structured participatory foresight study comprising two expert forecasting studios convened by The GovLab between 2025 and 2026. The studios brought together nineteen senior practitioners spanning official statistics, digital and trade policy, open science, AI governance, geospatial systems, and public-sector innovation across multiple jurisdictions. Applying a qualitative signal-scanning methodology grounded in the horizon-scanning and anticipatory-governance traditions, we elicited, clustered, and thematically synthesized emerging developments in data access, governance,and reuse, and stress-tested them against practitioner experience. We identify seven convergent signals: (1) the open-data paradigm is under strain; (2) data ecosystems are becoming machine-centric and AI-mediated; (3) inference is reshaping the foundations of data governance;(4) data infrastructure is becoming harder to sustain; (5) governance is fragmenting across institutions and jurisdictions; (6) sovereignty and security are driving a turn toward strategic control; and (7) data-sharing models require stronger incentives and benefit-sharing mechanisms. We further map the reinforcing feedback loops that couple these signals, showing how interventions in one domain propagate risks and opportunities across the wider ecosystem. We argue that data governance is becoming inseparable from AI governance, digital public infrastructure, economic strategy, democratic resilience, and geopolitical competition, and we outline an agenda for anticipatory data governance capable of adapting before dependencies, risks, and missed opportunities become locked in. The contribution is diagnostic rather than predictive: the signals offer an evidence-informed framework for reasoning about structural shifts already underway, not a forecast of specific technological outcomes…(More)”.
Article by Stanford HAI: “Millions of Americans are affected by mental illness every year, yet the cost of therapy remains out of reach for many, and a shortage of licensed clinicians means that even those with insurance often wait months for an appointment. Into this void has stepped a rapidly expanding market of AI-powered tools, including chatbots that provide therapeutic counseling and apps that offer cognitive behavioral therapy exercises on demand. Children and adults also seek out general-purpose chatbots like ChatGPT and “companion” bots such as those offered by Character.ai and Replika in times of loneliness or emotional distress.
There are promising potential upsides to the use of AI in mental health care: greater access, lower cost, tools that could extend the reach of an overstretched system, and a form of social and emotional support for people experiencing loneliness. But these promises also entail risk. Absent clear regulation and standardized third-party testing, these tools risk delivering substandard care and putting users in danger. News headlines abound about minors developing unhealthy emotional attachments to chatbots, users in crisis receiving harmful or inadequate responses, and research showing that general-purpose AI chatbots commonly miss warning signs.
AI’s role in mental health care is growing fast, and legislators are struggling to keep pace. To date, most legislative activities have happened in states, which have introduced more than 140 bills related to AI use in mental health contexts. Federal legislation is pending, but so far has been narrowly focused on protection of minors. This fragmented policy landscape is further hindered by a perpetually lagging evidence base: Many purpose-built AI mental health tools lack validated outcomes and representative samples, and are rarely evaluated with rigorous study designs. Meanwhile, the models powering general-purpose chatbots update so rapidly that safety research findings quickly become outdated…(More)”.
Blog by Milena Jael Silva: “Open data remains a democratic achievement, and a necessary one. It widened access to information once held inside ministries, laboratories, firms or platforms, and gave public actors, researchers and citizens stronger grounds for scrutiny and reuse. Yet access is no longer where the governance problem ends.
Data become powerful through what happens after publication. They are cleaned, linked, modelled, ranked, mapped and translated into decisions. A portal may release a signal, while the machinery that turns that signal into authority sits elsewhere. The sharper question is therefore not only whether data are open. It is whether public institutions and relevant communities retain the capacity to make, inspect and contest the claims made from them.
Take a coastal municipality. It publishes drainage maps, flood records, shoreline observations, land-use data, infrastructure files and social vulnerability indicators. The portal is functional, the licences permissive and the metadata adequate. By conventional open data standards, the municipality appears compliant. An external provider then combines those public signals with remote sensing, proprietary modelling and a hosted interface. It sells the municipality a climate-risk dashboard that ranks neighbourhoods, assigns exposure scores, proposes investment priorities and makes some zones appear less viable. Elected officials can see the colours, planners can export the maps and consultants can cite the ranking. Yet the municipality cannot reproduce the classifications, inspect all thresholds or fully argue with the uncertainty. Who, at that point, governs the coast?
This is closed inference: a post-publication asymmetry in which data may be open, shared or technically accessible, while the capacity to transform them into authoritative interpretation remains concentrated, closed or insufficiently accountable. It appears downstream, where accessible signals become classifications, forecasts, priorities and a working basis for public decisions.
At this point, the issue is not that information is hidden. The data may circulate, the dashboard may be visible, and the report may be public. Still, the authority to say what the data mean may sit inside an analytical infrastructure that public actors do not command. Transparency shows that information exists; it does not necessarily reveal how significance is assigned, how uncertainty is handled, or how an output becomes a reason to act…(More)”.
Report by New America: “Ballot initiatives are often criticized as too expensive and vulnerable to wealthy interests, but cost alone is a poor measure of democratic value. Drawing on campaign finance data, academic research, and interviews with practitioners, this report examines the costs, benefits, and relative value of statewide direct democracy in the United States. We find that initiative campaigns can be expensive, particularly in a handful of large states, but their costs are often comparable to lobbying and candidate campaigns. Money influences the initiative process, especially at the ballot-access stage, but does not reliably determine outcomes. Rather than restricting direct democracy, policymakers and organizers should pursue reforms that lower barriers to participation, strengthen voter information, and preserve initiatives as a viable pathway for citizen-led policymaking…(More)”.
Blog by Lea Gimpel: “The open web is undergoing a systemic structural transformation. Today, digital public goods (DPGs) and the broader knowledge commons face a critical challenge: large-scale, automated extraction by AI crawlers and scrapers operating without reciprocity. This leads to ballooning infrastructure costs for often volunteer-run, underresourced open projects, and the erosion of trust in open knowledge, among other challenges. DPG product owners have to make delicate decisions to protect their resources while keeping them as open as possible.
To help stewards of open resources navigate this complex landscape, the DPGA Secretariat is proud to share two complementary assets developed in consultation with our community:
What it is: A position paper and set of recommendations compiled directly by DPG product owners across major open initiatives like Wikipedia, Open Food Facts, Storyweaver, Govdirectory, The Turing Way, among others.
Core Focus: This note describes the problems faced by DPGs firsthand and outlines practical steps for engaging with commercial AI entities to ensure data users respect licenses, robot policies, and terms of service, and contribute back to the ecosystem, while also detailing technical mechanisms to protect against AI exploitation.
What it is: An overview of available mechanisms to protect against AI exploitation and how they map against the current open definition, and, by extension, the DPG Standard. The playbook’s aim is to deepen understanding and conceptual clarity around the tension between maintaining open access and ensuring the survival of an open resource.
Core Focus: The playbook helps understand the different layers of interventions available to DPG product owners, ultimately showing that code-level mechanisms are a first line of defence, but solving the challenge of AI exploitation requires ecosystem-level solutions at the policy, legal, and governance levels.
Layers of Defence: It introduces a 6-layer “defence and reciprocity pyramid” classifying technical, legal, regulatory and institutional measures..(More)”
Paper by Talia Caplan and Bilal A Mateen: “In 1600, Queen Elizabeth I granted the East India Company a royal charter and a monopoly over trade across much of the known world. For more than a century, the relationship was mutually advantageous: the Crown received revenue, access to rare commodities, and unparalleled geopolitical reach; the Company received protection, legitimacy, and the coercive backing of a state. By the mid-18th century, it became something very different. The Company maintained its own army, territories, and foreign policy, and increasingly operated in ways that were counter to the objectives of the Crown, resulting at times (due to its unconstrained, singular objective of maximising profits) in the deaths of millions.
Today, the world’s largest technology firms are at the precipice of a similar transition: towards becoming firms that function as corporate sovereigns whose power derives from the control of indispensable digital infrastructure and the algorithms that shape the attention economy, rather than from physical territory.2 The concentration of power is stark. A single firm (Nvidia) supplies between 80 and 90% of the chips on which advanced artificial intelligence (AI) models are trained; a handful of hyperscale providers (Microsoft Azure, Amazon Web Services, Google Cloud) operate the data centres in which those chips run; and a small number of laboratories (eg, OpenAI, Anthropic, Google DeepMind, DeepSeek), almost all American-owned, use those data centres to produce the frontier AI models on which others build.
These layers are less separate than they appear. A dense web of cross-investment now binds them, with chipmakers taking equity in the laboratories that buy their processors, and cloud providers and laboratories acquiring stakes in one another while committing to purchase each other’s services; analysts have estimated that interlocking arrangements of this type are worth more than US$800 billion. The effect is a tendency towards vertical integration achieved through finance rather than a formal merger, drawing nominally competing entities into a single, mutually dependent interest.
We expect that two structural features will characterise AI firms’ transition to pseudo-sovereign status. These firms will increasingly occupy every position in their own oversight, building the systems, funding the safety evidence (based on evidence from a preprint), staffing the advisory bodies, and drafting the standards meant to constrain them. They will also become jurisdictionally mobile in ways territorial regulators and sovereign governments are not, using digital infrastructure and complex corporate structures to obfuscate the residency of data and software in an effort to escape true oversight.
In that context, a health system that adopts a frontier AI solution for triage, imaging, or clinical decision support—such as the Horizon 1000 initiative seeks to roll out in Rwanda—does not necessarily acquire a substitutable tool but rather creates a dependency on a supply chain it neither controls nor can reproduce. The risks are especially acute in the global health context because upfront philanthropic support, especially when provided by the frontier AI companies that stand to benefit most, increases the likelihood of three specific risks…(More)”.