Stefaan Verhulst
Article by Stefaan Verhulst: “As artificial intelligence systems rapidly evolve and start to impact nearly every sector of society, the conversation around governance has mainly focused on models (and their output): their transparency, fairness, accountability, and alignment. Yet this focus, while necessary, is incomplete. AI systems are only as reliable, equitable, and effective as the data (input) on which they are trained and operate.
Data governance is not peripheral to AI governance — it is its bedrock.
At the same time, the rise of AI is not simply placing new demands on data governance; it is fundamentally transforming it. What counts as data, how it is curated, who has a say in its use, and which institutional arrangements govern it are all being reimagined in response to AI’s capabilities and risks.
This essay examines 10 key areas or shifts where data governance is being reshaped—either to accommodate AI or as a direct consequence of it…(More)”.
Article by Stefaan Verhulst and Despite decades of investment in statistical systems and open data initiatives, official data remains difficult to discover, interpret, and apply in practice. The challenge is no longer one of availability, but of (re)usability. This persistent gap underscores a broader paradox at the heart of contemporary data governance: data may be open, yet it remains functionally inaccessible for many intended users.
In this context, the International Monetary Fund has been a pioneer in exploring how artificial intelligence and open data can intersect to address this usability challenge. Its StatGPT: AI for Official Statistics report, by James Tebrake, Bachir Boukherouaa, Jeff Danforth, and Niva Harikrishnan, offers a timely and important contribution to this evolving conversation – pointing toward a future where AI can make official data more navigable, interpretable, and actionable.
The data challenge is no longer just about availability, but about (re)usability.
The report provides a detailed account of the friction users face across the data lifecycle. Even highly motivated users must navigate fragmented portals, inconsistent terminology, and siloed datasets, often spending significant time assembling information that should be readily accessible.
The result is a fragmented ecosystem in which metadata and data are distributed across institutions and platforms, forcing users to navigate multiple systems and standards—and to reconstruct context—before they can assess whether the data is re-usable.
This resonates strongly with broader observations across the open data ecosystem: access alone does not guarantee impact. Without the ability to meaningfully engage with data, openness risks becoming performative rather than transformative…(More)”.
Article by Mona Mourshed and Nalini Tarakeshwar: “Following international aid declines, philanthropy is searching for innovative ways to support non-profits and Global South governments in delivering service solutions where outcomes data plays a central role.
Achieving data-driven innovation requires more than gathering the right facts – it must generate change in daily routines. The global philanthropy sector is now waking up this idea.
Below are three key lessons from non-profits that have successfully deployed data in their work in the Global South, and seen real progression in their goals of driving meaningful system change.
Lesson 1: Data users respond far better to carrots than sticks
If government staff feel that something bad will happen should their data reveal underperformance, they are unlikely to gather it. Philanthropy can play a catalytic role by supporting projects that combine data usage with fresh incentives and support.
Generation India works with national and state-government entities in a public-private partnership structure funded equally by both. Previously, training providers in government-funded programmes were reimbursed largely on training and certification; those two milestones accounted for more than 70% of the government payment per learner. While the remainder of government payment per learner did include some outcomes metrics, such as job placement and three-month job retention, the process for proving these outcome metrics was cumbersome and lengthy, discouraging efforts in this direction. Further, since training providers had learned how to break even on the 70% of input-related payments, they were willing to forgo additional outcome-related payments. The combined result was a job placement rate of less than 25%.
To turn things around, the partnership of Generation India and government entities reduced the input payments linked to programme completion to 56% and increased outcomes compensation to 44%. In parallel, it introduced new payment milestones based on job placement within three months of programme completion and job retention at three- and six-months after the initial placement, both of which are verified by third parties.
There’s a similar playbook at the Brazilian Collaborative Leadership alliance, a partnership between the Lemann Foundation and federal, state and municipal governments, which reaches 70% of first and second graders in the country. To advance literacy, Lemann Foundation funds teacher training and provides better-quality textbooks for students at all participating schools. The state commits to joining the national literacy programme, which includes instruction materials and assessments of second grade students. The state also recognizes schools with the best results by granting their principals cash awards with an average value of $10,000. While the recognized schools receive 60-75% of the cash award immediately, they can only access the remaining 25-40% if they help another school in their community improve its literacy outcomes, which spurs an additional layer of support. Lastly, 2-5% of state tax revenue is given to municipality governments based on their performance against targets, with each free to decide how it uses these funds….(More)”.
Article by Hélène Landemore: “American democracy has a personality problem.
At its core, our political system is a popularity contest. Elections reward those who are comfortable performing in public and on social media, projecting confidence and dominating attention. This dynamic tends to select for so-called alpha types, the charismatic and the daring, but also the entitled, the arrogant and even the narcissistic.
This raises a basic but rarely asked question: Why are we filtering out the quiet voices? And at what cost?
Over the past two decades, my research on collective intelligence in politics, democratic theory and the design of our institutions shows that the system structurally excludes those I call, in my new book, “the shy.” By the shy I mean not just the natural introverts, but all the people who have internalized the idea that they lack power, that politics is not built for them, and who could never imagine running for office. That is, potentially, most of us, though predictable groups — women, the young and many minorities — are overrepresented in that category.
The early-20th-century British writer G.K. Chesterton once offered a striking and unusual metaphor for what democracy should look like. He wrote, “All real democracy is an attempt (like that of a jolly hostess) to bring the shy people out.” What would our democratic institutions look like if we took that metaphor seriously?
One answer — perhaps the most promising one we have at this time — can be found in citizens’ assemblies.
Citizens’ assemblies are large groups of ordinary people, selected by lottery, who come together to learn about a public issue, hear from experts and advocacy groups, deliberate with one another and make recommendations. Picture jury duty for politics. Through random selection, citizens’ assemblies reach deep into the body politic to bring even the initially unwilling to the table. Once seated, participants are given time, structure and support to find their voices and contribute to forming a thoughtful collective judgment…(More)”.
Paper by Kayla Schwoerer: “Despite widespread adoption of open government data (OGD) initiatives, actual use remains limited, raising questions about how these public digital platforms are designed and governed. Prior research highlights the importance of data quality and usability for encouraging OGD use, yet empirical evidence linking specific design choices to observed user behavior remains scarce. This study draws on affordance theory to examine how metadata design features embedded in open data platforms shape open data use. The analysis draws on primary data collected from 15 U.S. cities’ open data platforms (N = 5863) to first assess the extent to which government agencies actualize metadata affordances to promote data quality and usability then test the relationship between affordance actualizations and two observed measures of use: dataset views and downloads. Results show that multiple dimensions of metadata practice are strongly and consistently associated with OGD use, with some practices linked to substantially higher levels of open data use. Even within a shared platform environment, variation in how publishers provide metadata correspond to meaningful differences in how often datasets are accessed, highlighting that metadata governance is not merely a technical detail but a factor that materially shapes user engagement with open data…(More)”.
Article by Amrita Sengupta and Shweta Mohandas: “The rapid integration of artificial intelligence in healthcare settings raises questions about the adequacy of existing data protection frameworks, particularly the reliance on informed consent as the primary mechanism for legitimatising the collection and use of health data for AI model training. This paper examines whether informed consent, as operationalized under India’s Digital Personal Data Protection Act (DPDPA) 2023, can serve as a satisfactory legal and ethical basis for using health data in AI development.
Drawing on the historical evolution of consent from medical research contexts to contemporary digital data protection regimes, this paper demonstrates that consent-based frameworks face structural limitations when applied to AI systems. The analysis reveals a trifecta of consent challenges: patients must consent to medical procedures, to digital health record creation, and implicitly to future AI model training, often without comprehending the scope, purpose, or risks of data reuse.
This paper advances three broad analyses: first, the limitations of informed consent in data protection and operationalisation challenges in healthcare, the dilution of patient consent and autonomy in AI model training, and the role of anonymisation for use of data for AI. Recognizing these limitations, the paper proposes alternative governance frameworks that complement individual consent…(More)”.
Report by the Federation of American Scientists: “Local government and universities are critical to our communities. How do they work together? How can they support each other? How can we think differently about their relationship to one another – moving beyond big employers and land users to thinking about the fruits and labors of what the research community can do for local policy making.
The Civic Research Agenda is a multi-year, multi-partner study that is the first comprehensive reporting on the priority research needs of U.S. cities and counties. FAS has asked local governments directly about their research needs and pressing knowledge gaps that, if addressed, would help address their priority challenges and goals. It also provides an analysis of the supply side barriers (and recommendations) that will connect research to impact.
This report provides…
- research questions that are in demand by local governments; and
- specific recommendations for local governments and universities to improve and grow the research-to-impact pipeline for one simple purpose: make research actionable, understandable, and accessible to communities across the country…(More)”.
Article by Northwestern Innovation Institute: “Universities produce a vast number of scientific publications each year. Yet only a small share ultimately leads to patents, startups, or broader industry adoption. The challenge is not a shortage of ideas, but limited visibility into which discoveries — and the researchers behind them — are most likely to move toward commercialization.
A new platform developed at the Northwestern Innovation Institute, called InnovationInsights, is designed to make that hidden potential visible.
Using artificial intelligence and large-scale research data, the system helps technology transfer offices identify faculty, papers, and emerging research areas with strong commercial promise — including many discoveries that would otherwise remain outside the innovation pipeline.
At the core of the platform is a searchable interface built around two levels of insight: researchers and their individual publications.
Users can explore researcher profiles that bring together key signals related to translational activity, including publication history, recent high-impact work, invention disclosures and whether a researcher’s papers have been cited by company patents. These profiles allow innovation teams to quickly identify faculty whose work is influencing industry or to show patterns associated with future commercialization.
At the publication level, InnovationInsights assigns each paper a commercial potential score based on machine-learning models trained on decades of historical data linking research outputs to downstream outcomes. Users can rank papers by this score to identify emerging discoveries that may be ready for translation, even before any patent activity occurs.
The platform also tracks citations from company patents, offering a direct view of where academic research is being used in industrial innovation. By comparing commercial potential scores with patent influence,institutions can see both future opportunity and current industry relevance…(More)”.
Paper by Huw Roberts, Mariarosaria Taddeo, and Luciano Floridi: “Efforts to develop global governance initiatives for artificial intelligence (AI) have increased significantly in recent years. However, these initiatives have generally had a limited impact due to their vagueness, lack of authority and repetition. Several factors contribute to the difficulties in establishing effective global AI governance mechanisms, including geopolitical tensions, institutional gridlock, and the general-purpose and sociotechnical characteristics of AI. Developing politically legitimate governance mechanisms that can operate within these constraints and effectively compel behaviour change among government and industry actors is essential for building a more mature global AI governance ecosystem. In this article, we contribute to this aim by introducing a framework for evaluating the political legitimacy of global governance initiatives. It is designed to clarify why many global AI governance initiatives lack authority and to identify opportunities for more impactful international cooperation. We operationalise the framework by assessing global AI governance initiatives which address two international security problems: establishing regulation for lethal autonomous weapon systems and implementing safety testing for general-purpose AI…(More)”.
Article by Leif Weatherby and Benjamin Recht: “A recent Axios story on maternal health policy referenced “findings” that a majority of people trusted their doctors and nurses. On the surface, there’s nothing unusual about that. What wasn’t originally mentioned, however, was that these findings were made up.
Clicking through the links revealed (as did a subsequent editor’s note and clarification by Axios) that the public opinion poll was a computer simulation run by the artificial intelligence start-up Aaru. No people were involved in the creation of these opinions.
The practice Aaru used is called silicon sampling, and it’s suddenly everywhere. The idea behind silicon sampling is simple and tantalizing. Because large language models can generate responses that emulate human answers, polling companies see an opportunity to use A.I. agents to simulate survey responses at a small fraction of the cost and time required for traditional polling.
Phone polling has become exponentially harder. Web polling is too uncertain. Silicon sampling removes the messy, costly part of asking people what they think…(More)”.