Stefaan Verhulst

Article by Michelle Holko, John Wilbanks, and Sam Howell: “…Compute, talent, and capital are necessary for AI-enabled biotechnology, but biodata is the binding constraint. Without large, representative, and interoperable biological datasets, AI models cannot generalize, scale, or translate into real-world impact.

The application of AI to biotechnology carries profound promise for national power. From stronger, bio-based armor for U.S. warfighters to patching supply chain vulnerabilities with domestic biomanufacturing, the potential is as vast as biology itself. The country that leads in AI-enabled biology will set the pace not only in health and medical discovery but also in agriculture, industrial production, and potentially even future deterrence. Seizing this potential, however, will hinge on improving America’s access to high-quality, secure biodata that is designed specifically for AI.

Biodata holds the blueprints of life and has become a new form of strategic power in the age of AI. These data, including DNA, RNA, proteins, and metabolites, are foundational to innovation in bio-based materials, fuels, agriculture, and medicine.

The National Security Commission on Emerging Biotechnology’s 2025 final report concludes that dominance in biotechnology will “hinge on who controls the most complete, accurate, and secure biological datasets.” Biodata is a strategic asset for national power in the twenty-first century, analogous to advanced semiconductors or critical minerals. U.S. competitors, namely China, are moving fast to establish AI-bio leadership.

China’s Biotech Edge

China’s advantage in AI-enabled biotechnology is not simply scale, but also coordination. Beijing’s national strategies explicitly link biotechnology, big data, and artificial intelligence under directed planning, aiming to align data generation, compute resources, and industrial translation across sectors. One example is China’s non-invasive prenatal testing ecosystem: The domestic non-invasive prenatal testing market was valued at roughly $608 million in 2023 and is projected to exceed $1 billion by the end of the decade, reflecting widespread integration of genomic sequencing, hospital networks, and commercial bioinformatics services. Firms such as BGI Group operate large-scale sequencing and testing platforms (including the noninvasive fetal trisomy test) that generate and process substantial volumes of genomic data within an integrated ecosystem that spans clinical care, research, and industry. China has also rapidly expanded its domestic cell- and gene-therapy ecosystem, including multiple Chimeric Antigen Receptor T-cell therapy approvals and a growing clinical biomanufacturing base, shortening the path from research to deployment. At the same time, China is building the data substrate that makes AI-bio compounding possible: massive longitudinal health cohorts and national-level biodata platforms designed for large-scale integration and analysis…(More)”.

Article by Alberto Rodriguez Alvarez: “The next wave of AI will be defined by agentic systems that can take actions: query databases, navigate portals, retrieve records, and increasingly interact with public digital infrastructure at scale.

That shift is already showing up as traffic hitting government sites and services is becoming machine traffic. Some of it is benign (search and discovery). Some of it is ambiguous (scraping and automated browsing). And some of it could become actively harmful if AI agents can reserve scarce services, submit fraudulent requests, or generate volume that overwhelms public systems. 

The problem is that the government’s current interfaces were not designed for agent-to-government interactions, and the default state of the world has become improvisation: agents “figure it out” by scraping pages and guessing based on previous learning.

This is where Boston’s work becomes instructive. Rather than treating agents as something to block wholesale, or something to embrace without guardrails, Boston is experimenting with a middle path: build a governed, secure, and reliable layer that mediates how AI agent systems interact with government resources…(More)”.

Book by Nick Chater and George Loewenstein on “How Corporations and Behavioral Scientists Have Convinced Us That We’re to Blame for Society’s Deepest Problems”…: “Two decades ago, behavioral economics burst from academia to the halls of power, on both sides of the Atlantic, with the promise that correcting individual biases could help transform society. The hope was that governments could deploy a new approach to addressing society’s deepest challenges, from inadequate retirement planning to climate change—gently, but cleverly, nudging people to make choices for their own good and the good of the planet.

It was all very convenient, and false. As behavioral scientists Nick Chater and George Loewenstein show in It’s on You, nudges rarely work, and divert us from policies that do. For example, being nudged to switch to green energy doesn’t cut carbon, and it distracts from the real challenge of building a low-carbon economy.

It’s on You shows how the rich and powerful have repeatedly used a clever sleight of hand: blaming individuals for social problems, with behavioral economics an unwitting accomplice, while lobbying against the systemic changes that could actually help. Rather than trying to “fix” the victims of bad policies, real progress requires rewriting the social and economic rulebook for the common good…(More)”.

Blog by Santi Ruiz: “…Below are 10 lessons I’ve learned about handling government data:

1. Administrative data has major gaps. It’s not just that we don’t collect things we should; it’s also that information a system like SEVIS should collect just isn’t in that system. While some data gaps result from human error, others are the product of data collection systems that are leaky, or that just don’t exist. We simply cannot know things one might assume we do, like which visa-holders are currently in the country, or the employer of every working international student, because the departure dates and employer addresses of working international students are only present a fraction of the time in SEVIS. The federal government doesn’t know these things either. Failing to adequately maintain records and non-mandatory both result in inconsistent record-keeping. These gaps occur on every level as we decline to write down valuable information, neglect to write down everything we’re supposed to, and fail to hold on to everything we once wrote down.
2. When something seems off, it often is. Government datasets often have a small number of users; often a handful of civil servants in this or that agency. This means that inaccuracies can persist unnoticed for a surprisingly long time. If you encounter what seems like a major error in government data, it’s less likely to be a failure of your understanding than you might expect. In 2024, the US undercounted the number of international students by 200,000. The error went unnoticed for months until one diligent user contacted the agency responsible. The frequency of and methodology for data collection also change periodically, which leads to results that are technically correct, but also unintuitive and potentially misleading. Most quantitative disciplines rightly train students not to assume that the data is wrong until they’ve scrutinized their own work or their understanding of the data first. But if you’re working with certain kinds of government data, you should probably leap more quickly to suspect underlying data issues.
3. If it’s a question on a form, you can find data on it. Government administrative data is commonly just collated responses to the same questionnaire. Reading the forms which feed into it can tell you what it might contain, and where to find it. Since information isn’t always collected where you might expect, learning an agency’s paperwork can save you time, too. While investigating how many H-1B visas go to former international students, and how much they earn, my colleague Jeremy happened to realize that US Citizenship and Immigration Services collects information on someone’s wages and current immigration status when they file an I-129 Petition for a Nonimmigrant Worker. He learned this by talking to someone who knows USCIS paperwork like the back of their hand: an experienced immigration lawyer. Without realizing it, his analysis wouldn’t have been nearly as rich.
4. We’re not actually counting. Lots of government data is based on representative samples, and uses statistical methods to reach conclusions about the population at large. But that data is not produced by literally counting the population at large. This introduces various assumptions that can easily invalidate your findings if you forget to include them. The “irreversible demographic fact” claimed by politicians last year, that two million more Americans were employed than in the year prior, was the result of using data in ways the statistical agencies explicitly tell users not to. Jed Kolko describes how this statistic was actually a zero-sum accounting artifact, resulting in part from the fact that the population totals are pre-determined by the census, while nativity is not. Since the Current Population Survey measures variable immigrant and non-immigrant populations but is always scaled to match Census totals, any reduction in the reported foreign-born population will necessarily appear as an increase in the native-born population, even if it’s driven by changes in response rates rather than real departures…(More)”

Book by Vittorio Loreto, Vito D P Servedio, and Francesca Tria: “This book offers a unified quantitative framework for understanding the dynamics of novelty and innovation across biological, technological, and societal systems. It explores how first-time occurrences—ranging from everyday experiences to groundbreaking discoveries—can lead to subsequent breakthroughs. The content is organized into three main parts. The first part introduces essential theoretical tools for investigating the emergence of new ideas. The second part examines both classical and modern models that capture the evolution, interaction, and competition of innovations within complex systems. This section emphasizes the importance of models based on the concept of the ‘Adjacent Possible’, i.e., all those things—ideas, molecules, technologies— that are one step away from what actually exists. The final section presents empirical case studies that utilize computational and data-driven methods to uncover hidden patterns in the diffusion of novelty. A postface summarizes the main findings and provides insight into future directions for research. By synthesizing insights from theoretical and computational physics, complexity science, and social sciences, this work challenges traditional views on predictability and control. It demonstrates that the forces driving innovation are both serendipitous and systematic, offering new perspectives on how progress unfolds. This comprehensive approach provides valuable methodologies for researchers, students, practitioners, and the general public, making it an essential resource for anyone looking to understand the complex processes that shape our ever-evolving world…(More)”.

Article by Soren Kaplan: “…Nonprofits face growing pressure to do more with less: Rising demand, shrinking funding, and increasingly complex social issues often exceed the capacity—and mission—of any single organization. At the same time, because today’s most urgent challenges are interconnected and systemic, effectively addressing hunger, homelessness, education, or health equity requires a level of strategic coordination that few organizations can achieve on their own.

Most nonprofits operate in isolation from each other, for reasons that are easy to understand. Structural incentives such as funding models, branding, and board expectations often reinforce competition over collaboration. As a result, even when their missions overlap, organizations frequently compete for limited funding, volunteers, and visibility, duplicating services in some areas and while leaving needs unmet in others.

Five organizations in Contra Costa County, California, have been developing a new model to align their efforts focused on food insecurity without sacrificing their autonomy as distinct organizations. I would suggest that the example of The Food Security Collaborative offers a replicable blueprint that other social sector leaders can adapt to their local contexts, a model for how—rather than working in isolation—nonprofits can connect their missions, integrate data, share resources, and coordinate services to amplify impact across a shared system.

I call this model an “Impact Collaborative.”..(More)”.

Article by Mariel Lozada and Rina Chandran: “Among the thousands of candidates seeking election in Colombia’s parliamentary polls this month is an artificial intelligence avatar. Its creator hopes it will change the way Indigenous people in the country are represented, despite concerns of bias and access.

Gaitana — created by Carlos Redondo, and other members of the Zenú community —is the digital representation of two Indigenous candidates for Senate and congressional seats in the March 8 election. Named for a 16th-century revolutionary leader, Gaitana is depicted as a blue-skinned woman who is an environmentalist and animal rights advocate. The bot communicates in Spanish, and currently has more than 10,000 users.

Colombian law requires human candidates, so Redondo is competing for the Senate, and Alba Rincón, an anthropologist and sociologist from the Emberá Katío ethnic group, is running for the House of Representatives. On the ballot, though, they appear as IA, the Spanish acronym for artificial intelligence. If elected, Redondo and Rincón will occupy seats reserved for Indigenous people, and defer to the digital platform to seek consensus from their communities on all legislative matters, Redondo told Rest of World…(More)”.

Book edited by Steven Bernstein and William D. Coleman: “Globalization has challenged taken-for-granted relationships of rule in local, regional, national, and international settings. This unsettling of legitimacy raises questions. Under what conditions do individuals and communities accept globalized decision making as legitimate? And what political practices do individuals and collectivities under globalization use to exercise autonomy?

To answer these questions, the contributors to Unsettled Legitimacy explore the disruptions and reconfigurations of political authority that accompany globalization. Arguing that we live in an era in which political legitimacy at multiple scales of authority is under strain, they show that globalization has also created demands for regulation, security, and the protection of rights and expressions of individual and collective autonomy within and across multiple political and geographic spaces. Instead of offering simplistic arguments for or against global governance, enhanced democracy, or economic integration, the contributors provide a sophisticated examination of the complexities of legitimacy and autonomy in a globalizing world…(More)”.

Paper by Nicolien Janssens and Frederik van de Putte: “Recent years have seen an increase in the use of online deliberation platforms (DPs). One of the main objectives of DPs is to enhance democratic participation, by allowing citizens to post, comment, and vote on policy proposals. But in what order should these proposals be listed? This paper makes a start with the principled evaluation of sorting methods on DPs. First, we introduce a conceptual framework that allows us to classify and compare sorting methods in terms of their purpose and the parameters they take into account. Second, we observe that the choice for a sorting method is often ad hoc and rarely justified. Third and last, we criticise sorting by number of approvals (‘likes’), a method that is very common in practice. On the one hand, we show that if approvals are used for sorting, this should be done in an integrated way, also taking into account other parameters. On the other hand, we argue that even if proposals are on a par in terms of those other parameters, there are other, more appropriate ways to sort proposals in light of the approvals they have received…(More)”.

Essay by Stefaan Verhulst: “Today, the question facing governments is no longer whether they should use data. Especially in an age of Artificial Intelligence, that debate is long settled. The harder and more urgent question is how to govern data in ways that are trusted, durable, and fit for increasingly complex societal challenges. In short – how to make government data initiatives more effective and legitimate at the same time?

This question is where data stewardship steps in. Within a government setting, data stewardship is the practice of governing public-sector data as a shared civic asset, one whose value depends not only on technical performance but on legitimacy and institutional accountability. It begins from a recognition that data is a social artifact, embedded in social, political, and cultural processes. Only such a lived, non-technocratic approach can help local and state governments navigate the trade-offs, uncertainties, and value conflicts that increasingly define public-sector data use.

Limitations of the Current Approach

Too often, data strategy is framed as a technical exercise. But the shortcomings of a technocratic approach leave state and local data and innovation officers navigating tensions that technical solutions alone cannot resolve: promoting data sharing and reuse while also safeguarding privacy and civil liberties; delivering innovation and efficiency even as public skepticism and trust deficits deepen; and responding to growing pressure to deploy AI systems despite unclear lines of accountability and uneven institutional capacity. 

These are not engineering problems but governance challenges that require judgment, legitimacy, and sustained institutional stewardship. While current orientations toward data governance, focusing on  risk avoidance and compliance, continue to have an important role, today’s data requires socially embedded governance that centers legitimacy and public trust as facilitated by data stewards. 

The Vital Role of Data Stewards 

Data stewards move beyond today’s limited, technocratic approach, instead advancing—and embodying—a principle of legitimacy rather than merely compliance. This shift reframes the questions that guide data practice, from whether a particular use is legally permissible to whether it is socially appropriate, publicly understandable, and institutionally accountable…(More)”.