Stefaan Verhulst

Article by Stefaan G. Verhulst and Roshni Singh: “Artificial intelligence systems are increasingly designed to remember us. Whether answering a question, drafting an email, or recommending a course of action, modern AI systems draw on accumulated knowledge about a user’s preferences, behaviors, goals, and past interactions to function effectively. This capacity for context — persistent memory about who we are and what we do — is not a secondary feature. It is foundational to how these systems generate value.

But context in AI is more than a technical convenience or feature. It is also a source of risk. The accumulation and reuse of personal information introduces privacy vulnerabilities, particularly when data from different domains is aggregated into a single, unified memory. This is, of course, not a new concern: as Helen Nissenbaum argued in Privacy in Context, privacy depends on maintaining appropriate information flows within specific social contexts, and risks emerge when those boundaries are collapsed. What AI changes is the scale, speed, and inferential power of such aggregation, turning what were once discrete data linkages into continuous, dynamic systems capable of generating new insights, predictions, and vulnerabilities far beyond the original contexts in which the data was produced.

And the persistence of context raises deeper questions about cognitive dependence: when AI systems continuously shape the informational environment in which users think, they do not merely respond to us but influence how we understand ourselves and make decisions. In doing so, they risk constraining what we have described as digital self-determination: the ability of individuals and communities to meaningfully shape the conditions under which their data is (re) used and how it, in turn, shapes them — shifting agency from the user to the system in often opaque and difficult-to-contest ways.

These risks are not limited to one category of AI. They apply across AI systems that store and reuse user data — from large language models and recommendation engines to agentic systems that act autonomously on a user’s behalf. What this article examines is not a particular technology, but a structural feature common to many: the use of context as memory, and the tradeoffs that follow.

Context is often treated as the accumulation of user data, but this framing is incomplete. Context is better understood as the relational structure that gives information meaning by situating it within social, temporal, and functional relationships. It is not simply what is stored, but how information is organized, linked, and interpreted within a given frame. Without these relationships, data may remain present but lose meaning or be misapplied across situations. As Jessica Talisman further elaborates, this spectrum runs from statistical proximity to formal logical commitment; AI systems that conflate these distinct levels of relational strength risk treating correlation as meaning.

In what follows, we draw on emerging writing on AI memory, context, and human-AI interaction to explore three interconnected dimensions of this problem. First, we examine why context matters so much for AI performance, and why it is better understood as a relational structure than as simple data storage. Second, we analyze the privacy risks that arise when contextual boundaries collapse. Third, we consider the cognitive risks of persistent memory: the possibility that AI systems come to shape not only what users do, but how they think. Across these dimensions, we also consider the implications for digital self-determination — that is, the extent to which individuals and communities retain meaningful agency over how they are represented, interpreted, and acted upon within context-aware AI systems. These concerns are especially acute for children and young users, for whom both data exposure and cognitive development are at stake…(More)”.

Book edited by Patrick Dunleavy and Timothy Monteath: “Open science is a set of principles and practices that aims to make research from all fields accessible to everyone for the benefit of researchers and society as a whole. Doing Open Social Science: A Guide for Researchers is the first comprehensive book setting out the principles and practices of open research, tailored specifically for those in the social science disciplines, at every career stage, offering practical advice on how to make research more transparent, trustworthy and reusable.

Divided into four parts, the book explores the core principles and philosophy of open social science. Part II addresses how to improve the reproducibility of research through open approaches, including chapters on the principles and tools of documenting research as you go and on open data practices. Part III focuses on open practices within the qualitative social sciences. Chapters examine interview-based research, case studies and fieldwork, systematic documentation analysis, archival data and the role of openness in citizen (social) science. Part IV addresses shifting research cultures, with chapters on strategies for presenting research clearly and accessibly to maximise reach and impact and on open access publishing. The book ends with a discussion of the future of open social science. Ultimately, it argues, openness as a wider cultural change can renew the social sciences and the core foundations for academic progress in more dynamic and sustainable ways…(More)”.

Paper by Nicole Czaplicki, et al: “As part of the comprehensive Construction Re-engineering Initiative at the U.S. Census Bureau, alternative data sources are being considered to supplement or replace current data collection methods. For the Survey of Construction (SOC), which measures new residential construction, this includes observing housing starts from satellite imagery in place of the current interviews for housing starts conducted by field representatives. Satellite images are obtained monthly for a subset of places in the SOC sample. Convolutional neural network models are then applied to images to predict likely new residential construction projects, with the current focus being single-family housing starts. Several post prediction processing steps are applied including exclusions based on intersections with known buildings or roads, treatments for missing data due to cloud cover, and adjustments for the length of time between consecutive images, to ultimately produce place level estimates of housing starts. These place level estimates are then combined with the existing building permit level survey data to produce estimates of West South Central division level housing starts, an experimental data product from the Census Bureau…(More)”.

Report by Vinith Annam and Isaac Yoder: “This report is an advocacy tool for chief data officers (CDOs) looking to expand understanding of the CDO role and the conditions that contribute to its success. It identifies six archetypes of state-level CDO offices—clarifying common variations, the conditions that cause them, and possibilities that CDOs might aspire to.  

CDO offices have expanded across the country, yet no common model defines how they are structured, resourced, or positioned. Drawing on comparative survey data developed in partnership with the National Association of State Chief Information Officers (NASCIO) and self-reported data from the State Chief Data Officer (CDO) Tracker, this report examines how state CDO offices, and equivalent state data offices, are designed and operate across states, and the implications.

Key takeaways:

• There is no “optimal” model. An office’s archetype reflects tradeoffs among competing institutional priorities. It is dynamic, not linear, and offices blend characteristics as goals and maturity evolve. 
• Funding constraints are persistent across all maturity levels. Inadequate funding is a consistently cited challenge for the majority of states, indicating that resource pressures are structural.
• Reporting structure shapes strategic orientation. State CDOs remain predominantly aligned with IT leadership. While this enables execution of technical data initiatives at scale, it can limit a CDO’s ability to shape data strategy, governance, and policy.
• Some challenges and priorities evolve alongside data maturity, while others—particularly data quality and cross-agency data sharing—persist.
• Building strategic relationships and trust is essential. Strong partnerships with top administration officials and IT leadership are foundational for successfully implementing enterprise-level data strategies.

This report addresses the analytical gap in how offices with similar aspirations function so differently in practice. In doing so, it offers a tool for data leaders looking to increase their office’s funding and authority through strategic conversations with decision-makers and data management stakeholders…(More)”.

Article by Christopher Graziul and Cheryl M. Danton: “In March 2025, the European Union published the European Health Data Space (EHDS) regulation, creating a legal framework that will make the electronic health records of roughly 450 million residents available for secondary use by March 2029, including commercial product development, pharmaceutical research, and AI training (Regulation (EU) 2025/327, 2025). The system defaults to inclusion: citizens must opt out, and, currently, the opt-out is all-or-nothing, making no distinction between academic research and commercial pharmaceutical development. Seventeen leading scholars have warned that the framework risks enabling corporations to extract value from population health data without equitable benefit-sharing, producing a system where citizens bear both the data burden and the cost of products developed from it (Marelli et al., 2023). That is, the EHDS does not merely regulate existing sensitive open data. Rather, it creates a new category where governments convert private health records into commercially accessible information through legislative mandate.

This is the commodification of sensitive open data in real time. In a previous article, we addressed the governance challenge of sensitive open data: how to balance transparency and protection for personal data in public records like police radio transmissions and public health records (Danton & Graziul, 2026). This piece asks a different question: whose economic interests does inadequate governance serve? The answer, from Washington to Brussels to New Delhi, involves a global data brokerage industry that treats public records and government-collected personal data as raw material for commercial extraction (Grand View Research, n.d…(More)”.

Article by Mahvish Shaukat et al: “Many governments and policymakers rely on policy-advising organisations – international development banks, think tanks, ministries – to translate academic research into actionable recommendations. Yet better evidence does not automatically produce better policy. Even when high-quality research exists, it must travel through layers of hierarchy inside a policy-advising organisation, both upward and downward. A junior analyst may surface a finding that never reaches the decision-maker who could act on it. Equally, a senior leader’s review of the evidence may never filter down to the operational level. Each step in the chain is a potential bottleneck; accordingly, the evidence-to-policy pipeline increasingly impedes the use of rigorous research in practice (DellaVigna et al. 2024, Garcia-Hombrados et al. 2025, Bonargent 2024, Rao 2024).

A growing evidence base examines how policymakers engage with evidence (Vivalt and Coville 2023, Toma and Bell 2024), and how training can build capacity for evidence use (Crowley et al. 2021, Mehmood et al. 2024), but much less is known about what drives evidence diffusion within organisations. Who shares evidence with whom? Does it depend on where in the hierarchy evidence first lands? Do concerns about how peers might react shape whether sharing happens? These are the questions we set out to answer…(More)”.

Report by Reema Patel: “Our collective thinking about data governance is shaped by unconscious beliefs about the world. These are sometimes described as mental models. Our mental models shape our sense of what problems are noticed and what solutions to these problems are feasible and possible. They can sometimes limit our understanding of important issues such as how data can be governed and managed. Our current mental models about data are failing. The ongoing data trust deficit, public concern about data governance approaches, poor data quality, datasets with systemic bias and inequality that shape artificial intelligence, and repeated data governance systems failures. These all point to the need to dramatically reshape the way we think about data governance…

This report maps out ten different mental models of data governance. These are: data colonialism, data ownership, data control, data technocracy, data liberation, data protection, data justice, data sovereignty, data culture, and data stewardship. Understanding how we think about data governance as our mental models, I argue, is an essential first step towards moving beyond current approaches to realising a just and viable data governance future…(More)”.

Blog by Timber Stinson-Schroff: “…The mission of this institute is to advance the theory and practice of protocol design, analysis, and stewardship across domains, as well as promote protocol literacy, appreciation and cultural salience globally. In other words, our mission is to build the field and community capable of stewarding the ongoing planetary processes of protocolization – the slow, largely invisible means by which human behaviors become standardized into the coordinating infrastructure of civilization.

The Protocol Institute inherits the work of its predecessor, the Summer of Protocols (SoP) program, which ran from 2023 to 2025. The Ethereum Foundation initiated SoP with a bold thesis: deepened understanding of protocols generally would enable better governance of the core Ethereum protocol specifically. As a seasonal grants program, SoP was designed to:

• Bootstrap a new field of study around protocols
• Establish protocols as a first-class concept for thinking about and acting in the world
• Seed a scene and improve literacy around protocols

The program not only succeeded in these objectives, it went beyond them, sparking a rich discourse spanning many domains, such as robotics, climate, government, natural resources, insurance, programmable cryptography, economics, urban planning, health, gaming, encryption, wildfire management and more. Through its successes, both planned and unplanned, SoP has created the need for a suitable vehicle to sustain long-term activities building on what has already been accomplished…(More)”.

Paper by Valentine Goddard and Dr. Leslie Salgado Arzuaga: “The rapid expansion of generative artificial intelligence is profoundly reshaping how cultural and knowledge resources are created, shared, and governed, exposing significant gaps in existing frameworks for understanding, protection, and oversight. While intellectual property regimes (IPRs) remain one of the primary mechanisms available to artists, creators, cultural workers, and Indigenous knowledge holders to protect their work, safeguard cultural heritage, and derive fair value from their contributions, they are increasingly strained by the scale, speed and opacity of AI systems, which often rely on vast amounts of data drawn from public, proprietary and traditional knowledge sources. At the same time.

At the same time, these same frameworks can enable the privatization and appropriation of public domain knowledge and cultural commons with proprietary AI systems, creating tensions between artists’ economic rights, cultural sovereignty, and broader economic development rights, particularly for communities in the Global Majority. These impacts are gendered and intersectional, disproportionately affecting women and communities whose knowledge, labour and decision making authority have been historically undervalued or excluded, contributing to labour precarity, cultural erasure, and unequal access to decision making. Furthermore, there is also a clear lack of accessible, independent, and balanced information to support civil society, cultural actors, and policymakers in navigating these complex dynamics. In this context, the creation of a dedicated, civil society-led and collaboratively designed Repository emerged as a necessary response to facilitate knowledge sharing, surface diverse perspectives, share best practices, protect digital cultural sovereignty, and support more equitable, informed, rightsbased, and culturally sensitive approaches to AI governance in the cultural sphere…(More)”.

Report by Luca Picci and Jorge Rivera: “The development community is driving down a winding road while looking in the rearview mirror. Official statistics tell us where official development assistance (ODA) stood a year ago, with precision and authority. But they don’t tell us the road has turned until after the fact.

That’s a problem. It means that programme decisions, policy responses, and advocacy strategies are being made on the basis of incomplete or outdated data. In 2025, the four largest Development Assistance Committee (DAC) donors—the United States, Germany, the United Kingdom, and France—all cut ODA. Initial projections estimated that ODA would fall by between 9% and 17% in 2025 [1]; preliminary ODA figures published in April 2026 suggest that ODA fell by 23.1% [2]. Official detailed data for the 2025 cuts, disaggregated by donor, sector, and recipient, will not be available until December 2026, however, and detailed data on 2026 ODA will not be available until late 2027.

Nowcasting methods have been developed to address this problem. They estimate the current value of a lagged official statistic using data published at a higher frequency, updating estimates as new information arrives, and quantifying uncertainty. Nowcasting provides a blurred view through the windshield; still not a clear picture of the road, but enough to spot the turn.

This paper reviews the ODA data landscape and existing nowcasting approaches, and assesses which merit further investigation in the context of ODA. Our conclusion is that a nowcasting system for ODA is within methodological reach. Such a system could produce estimates that get updated as new information arrives, ahead of official data publication. Initially, this system would estimate ODA at the donor level for major DAC donors, with finer disaggregation contingent on what the available data can support…(More)”.