Stefaan Verhulst
Article by Cheryl M. Danton and Christopher Graziul: “…Data sovereignty is a critical issue for indigenous communities wary of extractive practices, a conversation that predates current debates (Kukutai and Taylor, 2016). We speak about the American context here, which is influenced by Canadian efforts to support First Nations (Carroll et al., 2020), but the tensions involved emerge in multiple contexts around the world (e.g., Australia, see Lovett et al., 2020). We cannot speak to these contexts individually but highlight relevant aspects of indigenous data sovereignty in the United States as an example.
The FAIR principles—published in 2016 to promote best practices in scientific data sharing—are designed to make data “Findable, Accessible, Interoperable, and Reusable” (Wilkinson et al., 2016). A complementary set of principles, the CARE Principles—“Collective Benefit, Authority to Control, Responsibility, and Ethics”—were developed by the International Indigenous Data Sovereignty Interest Group, through consultations with Indigenous Peoples, academic experts, government representatives, and other affected parties, in response to increasing concerns regarding the secondary use of data belonging to Indigenous communities. According to their authors, the CARE Principles integrate Indigenous worldviews that center “people” and “purpose” to address critical gaps in conventional data frameworks by ensuring that Indigenous Peoples benefit from data activities and maintain control over their data (Carroll et al., 2020)…(More)“
Paper by Giliberto Capano, Maria Tullia Galanti, Karin Ingold, Evangelia Petridou & Christopher M. Weible: “Theories of the policy process understand the dynamics of policymaking as the result of the interaction of structural and agency variables. While these theories tend to conceptualize structural variables in a careful manner, agency (i.e. the actions of individual agents, like policy entrepreneurs, policy leaders, policy brokers, and policy experts) is left as a residual piece in the puzzle of the causality of change and stability. This treatment of agency leaves room for conceptual overlaps, analytical confusion and empirical shortcomings that can complicate the life of the empirical researcher and, most importantly, hinder the ability of theories of the policy process to fully address the drivers of variation in policy dynamics. Drawing on Merton’s concept of function, this article presents a novel theorization of agency in the policy process. We start from the assumption that agency functions are a necessary component through which policy dynamics evolve. We then theorise that agency can fulfil four main functions – steering, innovation, intermediation and intelligence – that need to be performed, by individual agents, in any policy process through four patterns of action – leadership, entrepreneurship, brokerage and knowledge accumulation – and we provide a roadmap for operationalising and measuring these concepts. We then demonstrate what can be achieved in terms of analytical clarity and potential theoretical leverage by applying this novel conceptualisation to two major policy process theories: the Multiple Streams Framework (MSF) and the Advocacy Coalition Framework (ACF)…(More)”.
Paper by Bogdan Pahonțu et al: “Technological advances are increasingly influencing how the public sector makes decisions according to citizens’ needs and the community’s problems. The need for a solution that facilitates the fast adaptation of administration to social context and people’s feedback becomes mandatory in order to ensure better services and implement projects that are in concordance with needs. In this paper, we propose a sandbox solution that helps public administration better understand community problems in real time, allocate public money more effectively to projects that really matter, and assess the administration’s performance. We started by collecting, filtering and analyzing social platforms posts and comments for 95 municipalities, and we extracted both the impressions/sentiment, but also the real problems that the communities are facing. Also, we categorized all cities depending on population, geographical area, and historical area to better identify common problems and create clusters of topics based on this split. We identified the most common issues communities face and integrated all the information into a sandbox that can be easily used by local administration for reactive decision-making and by central administration to provide a better overview of how public money is spent and whether the decisions align with needs. The results show that there is a real need for a sandbox to bring more clarity to the central and local administration layers and also better connect administrations with the people…(More)”.
About: “The Deportation Data Project collects and posts public, anonymized U.S. government immigration enforcement datasets. We use the Freedom of Information Act to gather datasets directly from the government, and we also post datasets that the government has posted proactively or in response to others’ requests. We expect these datasets to be used by journalists, researchers, lawyers, and policymakers.
- We post mostly individual-level datasets. Individual-level data is most useful to those with a background in data analysis, allowing these users to make their own decisions about how to analyze the data.
- We write documentation for each dataset, including its limitations and how it was obtained. We also provide a codebook for ICE data, which includes information about the variables in the dataset, their definitions, and their values. In addition, we post documentation from the agencies.
- We are grateful for the work of others, and especially of the Transactional Records Access Clearinghouse (TRAC), in first obtaining access to many of these datasets. Please get in touch if you have done relevant work in this area and we can do more to acknowledge your contributions…(More)”.
Implementation plan by the Australian Government: “Australian Government entities are required to embed the Australian Government Framework for governance of Indigenous data. In response, the Department of Health, Disability and Ageing has co-designed an implementation plan with Aboriginal and Torres Islander and non-government partners to ensure that First Nations people are afforded the right to exercise ownership and control over Indigenous data.
The Department holds substantial data assets about the Australian population, including data related to Aboriginal and Torres Strait Islander (First Nations) people.
The plan aims to build strong governance over the Department’s Indigenous data holdings by implementing actions aligned with the following six goals:
- embed governance of Indigenous data in Department policies and processes
- build and maintain meaningful partnerships with First Nations people, communities and organisations
- develop and implement methods for First Nations people to know what data are held relating to their interests, its use, and how it can be accessed
- build governance of Indigenous data capability for Department staff and First Nations partners
- support and engage in organisational and cultural change to improve governance of Indigenous data across the government
- monitor and evaluate governance of Indigenous data implementation…(More)”
Framework by Josh Martin: “The first 90 days as a Chief Data Officer can make or break your tenure. You’re walking into an organization with high expectations, complex political dynamics, legacy technical debt, and competing priorities. Everyone wants quick wins, but sustainable change takes time. I learned this the hard way when I became Indiana’s Chief Data Officer in 2020—right as COVID-19 hit. Within weeks, I was leading the state’s data response while simultaneously building an agency from scratch. The framework below is what I wish I’d had on day one. This isn’t theory. It’s a battle-tested playbook from 13 years in state government, leading a 50-person data agency, navigating crises, and building enterprise data governance across 120+ agencies…(More)”.
Article by Adam Milward: “According to a recent Request for Information published in the Federal Register, ICE is seeking details from U.S. companies about “commercial Big Data and Ad Tech” products that could directly support investigative work.
As WIRED has reported, this appears to be the first time ICE has explicitly referenced ad tech in such a filing — signalling interest in repurposing technologies originally built for advertising, such as location and device data, for law-enforcement and surveillance purposes.
ICE has framed the request as exploratory and planning-oriented, asserting a commitment to civil liberties and privacy. However, this is not happening in isolation. ICE has previously purchased and used commercial data products — including mobile location data and analytics platforms — from vendors such as Palantir, Penlink (Webloc), and Venntel.
What are the implications for commercial organisations?
This kind of move by ICE throws a spotlight on the moral responsibilities of data-heavy companies, even when what they’re doing is technically legal.
I strongly believe in data federation and meaningful data sharing between public and private sectors. But we must be honest with ourselves: data sharing is not always an unqualified good.
If you’re sharing data or data tools with ICE, it seems reasonable to suggest you’re contributing to their output – at the moment this is certainly not something I, or MetadataWorks as a company, would be comfortable with.
For now, most of these private companies are not legally forced to sell or share data with ICE.
In essence:
- For the private sector, choosing to sell or share data or data tools is an ethical as well as a financial decision
- Choosing not to sell is also a statement which could have real commercial implications..(More)”.
Article by Meghan Maury: “The Privacy Act of 1974 was designed to give people at least some control over how the federal government uses and shares their personal data. Under the law, agencies must notify the public when they plan to use personal information in new ways – including when they intend to share it with another agency – and give the public an opportunity to weigh in.
At dataindex.us, we track these data-sharing notices on our Take Action page. Recently, a pattern has emerged that you might miss if you’re only looking at one notice at a time.
Since around July of last year, the number and pace of data-sharing agreements between federal agencies and the Department of the Treasury has steadily increased. Most are framed as efforts to reduce “waste, fraud, and abuse” in government programs…
It might be. Cutting waste and fraud could mean taxpayer dollars are used more efficiently, programs run more smoothly, and services improve for the people who rely on them.
I’ve personally benefited from this kind of data sharing. When the Department of Education began pulling tax information directly from the IRS, I no longer had to re-enter everything for my financial aid forms. The process became faster, simpler, and far less error-prone…
The danger comes when automated data matching is used to decide who gets help (and who doesn’t!) without adequate safeguards. When errors happen, the consequences can be devastating.
Imagine a woman named Olivia Johnson. She has a spouse and three children and earns about $40,000 a year. Based on her income and family size, she qualifies for SNAP and other assistance that helps keep food on the table.
Right down the road lives another Olivia Johnson. She earns about $110,000 a year, has a spouse and one child, and doesn’t qualify for any benefits.
When SNAP runs Olivia’s application through a new data-matching system, it accidentally links her to the higher-earning Olivia. Her application is flagged as “fraud,” denied, and she’s barred from reapplying for a year.
This is a fictional example, but false matches like this are not rare. In many settings, a data error just means a messy spreadsheet or a bad statistic. In public benefit programs, it can mean a family goes hungry…(More)”
The GovLab: “…we are launching the Observatory of Public Sector AI, a research initiative of InnovateUS, and a project of The Governance Lab(opens in new window). With data from more than 150,000 public servants, the Observatory represents one of the most comprehensive empirical efforts to date to understand awareness, attitudes, and adoption of AI as well as the impact of AI on work and workers.
Our goal is not simply to document learning, but to translate these insights into a clearer understanding of which investments in upskilling lead to better services, more effective policies, and stronger government capacity.
Our core hypothesis is straightforward: the right investments in public sector human capital can produce measurable improvements in government capability and performance, and ultimately better outcomes for residents. Skill-building is not peripheral to how the government works. It is central to creating institutions that are more effective, more responsive, and better equipped to deliver public value.
We are currently cleaning, analyzing, and expanding this dataset and will publish the Observatory’s first research report later this spring.
The Research Agenda
The Observatory is organized around a set of interconnected research questions that trace the full pathway from learning to impact.
Our goal is not simply to document learning, but to translate these insights into a clearer understanding of which investments in upskilling lead to better services, more effective policies, and stronger government capacity.
We begin with baseline capacity, mapping where public servants start across core AI competencies, identifying where skill gaps are largest, and distinguishing individual limitations from structural constraints such as unclear policies or restricted access to tools.
We then examine task-level use, documenting what public servants are actually doing with AI.
Our data also surface organizational obstacles that shape adoption far more than skill alone. Across agencies, respondents cite inconsistent guidance, uncertainty about permissions, and limited access as primary barriers.
Through matched pre- and post-training assessments, we measure gains in technical proficiency, confidence, and ethical reasoning. We plan to track persistence through three to six-month follow-ups to assess whether skills endure, reshape workflows, and diffuse across teams.
We analyze how training shifts confidence and perceived value, both of which are essential precursors to behavior change. We collect indicators of effectiveness through self-reported workflow improvements that can later be paired with administrative performance data.
Finally, we examine variation across roles, agencies, and geographies, how workers exercise judgment when evaluating accuracy, bias, and reliability in AI outputs, and how different training modalities compare in producing durable learning outcomes…(More)”
Article by David Oks: “Here’s the story of a remarkable scandal from a few years ago.
In the South Pacific, just north of Australia, there is a small, impoverished, and remote country called Papua New Guinea. It’s a country that I’ve always found absolutely fascinating. If there’s any outpost of true remoteness in the world, I think it’s either in the outer mountains of Afghanistan, in the deepest jungles of central Africa, or in the highlands of Papua New Guinea. (PNG, we call it.) Here’s my favorite fact: Papua New Guinea, with about 0.1 percent of the world’s population, hosts more than 10 percent of the world’s languages. Two villages, separated perhaps only by a few miles, will speak languages that are not mutually intelligible. And if you go into rural PNG, far into rural PNG, you’ll find yourself in places that time forgot.
But here’s a question about Papua New Guinea: how many people live there?
The answer should be pretty simple. National governments are supposed to provide annual estimates for their populations. And the PNG government does just that. In 2022, it said that there were 9.4 million people in Papua New Guinea. So 9.4 million people was the official number.
But how did the PNG government reach that number?
The PNG government conducts a census about every ten years. When the PNG government provided its 2022 estimate, the previous census had been done in 2011. But that census was a disaster, and the PNG government didn’t consider its own findings credible. So the PNG government took the 2000 census, which found that the country had 5.5 million people, and worked off of that one. So the 2022 population estimate was an extrapolation from the 2000 census, and the number that the PNG government arrived at was 9.4 million.
But this, even the PNG government would admit, was a hazy guess.
About 80 percent of people in Papua New Guinea live in the countryside. And this is not a countryside of flat plains and paved roads: PNG is a country of mountain highlands and remote islands. Many places, probably most places, don’t have roads leading to them; and the roads that do exist are almost never paved. People speak different languages and have little trust in the central government, which simply isn’t a force in most of the country. So traveling across PNG is extraordinarily treacherous. It’s not a country where you can send people to survey the countryside with much ease. And so the PNG government really had no idea how many people lived in the country.
Late in 2022, word leaked of a report that the UN had commissioned. The report found that PNG’s population was not 9.4 million people, as the government maintained, but closer to 17 million people—roughly double the official number. Researchers had used satellite imagery and household surveys to find that the population in rural areas had been dramatically undercounted.
This was a huge embarrassment for the PNG government. It suggested, first of all, that they were completely incompetent and had no idea what was going on in the country that they claimed to govern. And it also meant that all the economic statistics about PNG—which presented a fairly happy picture—were entirely false. Papua New Guinea had been ranked as a “lower-middle income” country, along with India and Egypt; but if the report was correct then it was simply a “lower-income” country, like Afghanistan or Mali. Any economic progress that the government could have cited was instantly wiped away.
But it wasn’t as though the government could point to census figures of its own. So the country’s prime minister had to admit that he didn’t know what the population was: he didn’t know, he said, whether the population is “17 million, or 13 million, or 10 million.” It basically didn’t matter, he said, because no matter what the population was, “I cannot adequately educate, provide health cover, build infrastructures and create the enabling law and order environment” for the country’s people to succeed…(More)”.