Stefaan Verhulst

Policy Memo by Anna Lenhart: “AI systems are regularly used to make decisions that directly impact individuals, from who gets a housing voucher to who gets a job, to bail—contexts with a long history of social disparities, facilitating encoded discrimination. The designs of these consequential AI decision systems are shaped by corporations and increasingly overseen by governments with little input from the public, specifically from users and individuals impacted by these decisions.

Executive branch agencies frequently engage the public in policy decisions via requests for comment and town halls. For decades, the Food and Drug Administration (FDA) has gone beyond traditional agency engagement processes via the Patient Representative Program (PRP), which recruits, trains, and embeds patients into oversight of the pharmaceutical industry, including decisions regarding clinical trial design, endpoints (evaluation metrics), risk/benefit analysis, product labeling, etc. This memo proposes creating a Decision Subject Representative Program inspired by the FDA’s Patient Representative Program. 

While pharmaceutical drugs and consequential AI decision systems vary in scope and impact, both technologies need to be safe and effective to be trusted by the public and consumers. Public engagement has long been a tool for building trust and legitimacy in governance decisions while providing a complement to expertise associated with elite institutions. Three decades of FDA experience in systematizing patient engagement offer valuable inspiration for AI governance. Specifically, the General Services Administration (GSA) should pilot embedding Decision Subject Representatives into the procurement process for consequential AI decision systems, the National Institute of Standards and Technology (NIST) should pilot engaging Decision Subject Representatives in efforts to shape standards, and Congress could add a flexible Decision Subject Representatives Program (DSRP) to new regulatory proposals…(More)”.

Paper by Paulius Jurcys et al: “The advent of AI twins (digital replicas encapsulating individuals’ knowledge, psychological traits, memories, beliefs, preferences, values, and behaviors) raises distinctive legal and ethical challenges. These entities, derived from personal data, compel a re-evaluation of what it means to have dominion over one’s data, and what it is to be a person or to have a personal identity in an AI-mediated age. The argument proceeds in three steps. First, we offer a taxonomy of AI twins and distinguish them from AI assistants and agents. Second, we show that current legal frameworks privilege the technological infrastructure (the “shell”) over the personal data (the “ghost”) that gives each AI twin its distinctive identity and value. Third, we argue that when individuals consolidate their personal data into self-controlled environments, the classical requirements for recognising property (a clearly defined asset, exclusive control, and value to the owner) are met. Ownership of the underlying data grounds a claim to ownership of the AI twin built from it. The article therefore defends a “human-centric” approach to data governance which is grounded in a reimagined social contract that prioritizes individual sovereignty and a private-by-default approach to data. The goal is to empower individuals, ensure equitable data stewardship, and address the socio-legal realities of AI-driven identities…(More)”.

Article by Vanessa Lyon et al: “…Because agentic systems can plan, decide, and act across workflows with limited human intervention, they alter how data is accessed, combined, stored, and propagated across the enterprise.

Autonomous execution allows agents to modify records and trigger transactions in real time. Cross-system orchestration moves data across platforms, APIs, and third-party environments as part of multistep tasks. Instructions or errors can cascade beyond their original scope. At the same time, contextual memory layers such as prompts and vector stores may retain sensitive information. Agent-generated outputs can inherit the sensitivity of underlying data while escaping established classification models.

Agentic AI also expands exposure; it can involve integration with third-party systems, data providers, and external tools. As agents execute workflows, they interact with APIs, partner platforms, and external models, operating beyond enterprise-controlled environments. This reduces control, creates dependency on external governance, and diffuses accountability.

As agents execute workflows across systems, data can expand exposure and make unintended changes harder to contain. These dynamics are most visible in five risk categories:

• Propagation Risks. Data moves beyond its intended boundaries, spreading exposure across systems and third-party environments and increasing the chance that errors or unauthorized changes will cascade.
• Persistence Risks. Sensitive information remains in prompts, embeddings, caches, or logs beyond its intended lifetime, creating long-term leakage and compliance challenges.
• Autonomy Risks. Agents act or decide beyond their mandate, modifying records or triggering downstream processes without human oversight.
• Emergence Risks. Interactions among multiple agents or components produce compounded behaviors or unexpected outcomes, amplifying harm.
• Third-party Risks. Reliance on external systems introduces dependency, inconsistent control enforcement, and reduced clear accountability.

These categories help represent concrete failure modes that policies, controls, and architecture must address. In parallel, data quality emerges as a foundational risk vector in agentic environments. Unlike traditional settings where poor data quality might result in inaccurate reporting, agentic systems can act on flawed data in real time, triggering decisions and downstream processes before human intervention is possible. This elevates data quality to a governance priority as completeness, timeliness, and semantic consistency directly shape the safety of autonomous actions…(More)”.

Policy Brief by the ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S) and the Centre of Artificial Intelligence and Digital Ethics (CAIDE): “…outlines two possible models for the design of an effective and systemic statutory Digital Duty of Care.

• A risk-based model, which would require digital platforms to assess and take reasonable steps to prevent and mitigate the risk of harm arising from the design, operation and use of their services.
• An outcomes-based model, which would require platforms to actively enable a safe, inclusive, healthy and rights-respecting digital environment in the long term.

Strengthening accountability and transparency

The researchers argue that the effectiveness of any Digital Duty of Care will depend on robust accountability frameworks that govern how platforms operate, curate content for, and facilitate engagement between users.

They note that existing transparency measures, which often rely on aggregated reporting, are not sufficient to explain how algorithmic systems shape users’ online experiences.

Instead, they call for new legal and technical infrastructure to enable users, regulators and civil society to meaningfully monitor and observe platform behaviour.

Call for a national platform observatory

A key recommendation of the brief is the establishment of a dedicated national platform observatory, with the mandate and resourcing to track how algorithmic systems target and curate content for Australians.

The observatory would also be responsible for collecting and analysing the information needed to assess whether platforms are complying with the proposed Digital Duty of Care.

Researchers argue that such infrastructure is essential to ensure the transparency, accountability and effectiveness of the new regulatory framework, and to support long-term public trust in digital platforms..(More)”.

Article by Rebecca Winthrop: “…Brainstorming is the work that’s fundamental to writing. As a researcher studying A.I.’s effects on education, I have concluded that these tools only superficially improve writing. The bigger and more alarming impact they have is to constrict our full range of thoughts and our ability to generate original and useful ideas — what we call creative thinking. This seems to be especially true for students. A.I.’s smooth sentences, elegant transitions and rich vocabulary give the illusion of expansive creativity and individuality. But the underlying ideas often converge into a few homogenized categories.

The erosion of creative thinking means young people will struggle to navigate uncertainty. Workers will strain to adapt to a shifting labor market. And society will miss out on the new ideas that can solve complex problems and enhance lives.

For the past eight years, the Georgetown University neuroscientist Adam Green has been leading a national research team tracking the range of novel ideas that college-bound high school students present in their application essays, before and after the introduction of ChatGPT. In one study, he and his team examined personal statements from more than 370,000 students, and found that after ChatGPT became available, their essays suddenly used diverse and colorful language, but lacked truly creative ideas. And the linguistic coverup worked; post-ChatGPT essays were rated as more “creative” by human judges, even if the substance of the essays trod familiar territory…(More)”.

Article by Jennifer Gibson and Kaitlin Thaney: “The global research enterprise relies on information infrastructure to power scientific discovery, medical breakthroughs, and evidence-based policymaking. But the data repositories, digital asset management services, and preservation systems that ensure research data remains open and accessible are often overlooked—until they disappear. Many of these tools and services are vulnerable to policy changes and funding cuts. Over the last 25 years, nearly 200 research data repositories have shut down permanently; more than half of those closures have happened since 2018.

Each closure represents lost knowledge and leads to broken links, bad citations, and a general inability to utilize and verify scientific findings. For example, without funding from the National Oceanic and Atmospheric Administration, the Alaska Earthquake Center has ceased providing real-time seismic data to inform tsunami warnings for the whole US West Coast. On topics as disparate as Gulf War illness or natural selection, when a repository goes dark, it can affect individuals or even entire research disciplines.

The data repositories, digital asset management services, and preservation systems that ensure research data remains open and accessible are often overlooked—until they disappear.

And because repositories and other open science infrastructures are commonly designed to support transboundary research, their collapse can have compounding global effects. In early 2025, the United States Agency for International Development (USAID) suspended access to the Demographic and Health Surveys (DHS) Program databases, a repository containing decades of population, health, HIV, and nutrition data from more than 90 countries. Almost overnight, researchers in Malawi lost access to critical data informing antiretroviral therapy programs serving roughly one million HIV-positive patients; researchers in Nigeria had nowhere to store new data designed to identify causes of maternal deaths; and the release of complete data from a 2023–2024 key indicators survey in the Democratic Republic of the Congo was delayed for months. After USAID was dismantled in the first half of 2025, an emergency grant from the Gates Foundation restored access to existing DHS data and selected surveys. But this three-year support has not returned the program to its prior scale, leaving 23 countries with surveys still incomplete or unanalyzed…(More)”.

This study by CanTrust Hosting Co-operative and Hypha Worker Co-operative: “…examines whether a genuinely co-operative alternative is feasible: one that preserves data sovereignty, democratic governance, and environmental integrity without asking organisations to accept inferior tools or prohibitive costs. Our report covers:

• Market analysis,
• Technical feasibility (including original research on energy consumption),
• Risk assessment,
• And financial viability.

What we found was that such an alternative is both technically achievable, and clearly needed…(More)”.

Book by Sarah O’Connor: “A tsunami of change, we are told, is sweeping the economy as robots and AI threaten to take over tasks done by humans. But while we worry that we’re robotizing our work, what if the real risk is that we’re robotizing ourselves?

When prize-winning Financial Times journalist Sarah O’Connor set out to investigate what was happening on the front lines of technological change, she found people who weren’t losing their jobs to machines, but who felt they were losing something else instead. From translators forced to edit AI output to university graduates interviewed by software and warehouse workers surrounded by robots, she heard stories of work becoming lonelier, less creative, less human.

But O’Connor also found hopeful stories of jobs being made better, safer and more enjoyable – where workers haven’t rejected the new tools, but instead have learned to control them. Exploring questions of power, design, institutions and ideas, her reporting shows that the way technology changes the world of work is not pre-determined, but must be contested and shaped by all of us.

Inspired by stories from nineteenth-century English cotton mills to twenty-first century Swedish mines, We Are Not Machines reveals how we can fight for work which is more respectful of our limits, and more worthy of our minds…(More)”.

Article by Sarah O’Connor: “Arguments about the past are often used as proxies for arguments about the future. It is no surprise, then, that a long-running debate about the Industrial Revolution has flared up as we begin another phase of rapid technological change.

The argument concerns whether the industrial revolution was good or bad for workers in the short run (and, by extension, which the AI revolution will be). The discourse in tech circles can be boiled down to this: “Relax: the industrial revolution led to higher real wages and more jobs”. “But don’t you know about ‘Engels’ pause’? Between 1790 and 1840, profits rose but real wages barely budged.” “Ah, but don’t you know that a different measure of real wages tells a different story?” And so on.

I find this argument perplexing — not because there are no lessons to be learnt from the industrial revolution, but because I don’t think these databases are the right place to look for them.

For one thing, the data on that era is patchy and unreliable. For another, the industrial revolution in Britain took place against a very different institutional backdrop. There was no universal suffrage, no legal trade unions and no modern welfare state. Indeed, you could argue these were eventual social responses to the industrial revolution. It is hard to see why we should expect the wage-setting dynamics of the past (even if we could agree on what they actually were) to repeat themselves today.

But most importantly, these quantitative metrics do not capture how profoundly the industrial revolution changed the nature of work for many people, in ways both good and bad. As the historian EP Thompson puts it in The Making of the English Working Class, “some of the most bitter conflicts of these years turned on issues which are not encompassed by cost-of-living series”: health, working hours, child labour, security and independence…(More)”.

Paper by Sara Thabit, Till Degkwitz, Mahardika Fadmastuti and Luca Mora: “Technology decentralisation is increasingly proposed as a key feature to build more trustworthy, accessible, and innovative digital public infrastructure, yet there is limited empirical knowledge of the actual benefits that such decentralised approaches would create from a public sector perspective. Furthermore, existing literature often relies on a linear dichotomy between data supply and demand that fails to capture the complexity of decentralised data ecosystems. This paper addresses these gaps by adopting an assemblage thinking perspective to conceptualise data platforms as complex socio-technical arrangements, and by developing a public values framework to broaden the understanding of the various outcome that data platforms can create. We apply this approach to an exploratory case study of Hamburg’s Urban Data Platform (UDP). Our findings demonstrate that public value creation is not determined by technical decentralisation alone but by specific architecture-governance configurations. We illustrate that both decentral and central practices can co-occur within the same system, where the role of a leading orchestrator is crucial to drive public value creation…(More)”.