Stefaan Verhulst

Paper by Charles I. Jones: “Artificial intelligence (A.I.) will likely be the most important technology we have ever developed. Technologies such as electricity, semiconductors, and the internet have been transformative, reshaping economic activity and dramatically increasing living standards throughout the world. In some sense, artificial intelligence is simply the latest of these general purpose technologies and at a minimum should continue the economic transformation that has been ongoing for the past century. However, the case can certainly be made that this time is different. Automating intelligence itself arguably has broader effects than electricity or semiconductors. What if machines—A.I. for cognitive tasks and A.I. plus advanced robots for physical tasks—can perform every task a human can do but more cheaply? What does economics have to say about this possibility, and what might our economic future look like?..(More)”.

Report by the IPPR: “Already, 24 per cent of people report using AI for information seeking every week. But there is widespread concern that the information provided will be inaccurate or biased, and that the rise in AI will threaten news organisations’ survival. As these risks materialise and undermine trusted information flows, we are missing opportunities for AI to become a positive force within the news ecosystem. 

At present, AI acts as an opaque and at times unreliable interface for news, with AI companies making invisible editorial choices that reshape the public’s access to information. It’s also beginning to erode existing financial incentives to produce news, without a clear sense of how high-quality journalism will be financed in the future.

This direction for AI and news is not inevitable, and a more positive transformation is possible. If we act soon, this moment can in fact be an opportunity for a reset…(More)”.

Article by Miklós Koren, Gábor Békés, Julian Hinz, and Aaron Lohmann: “Generative AI is changing how software is produced and used. In vibe coding, an AI agent builds software by selecting and assembling open-source software (OSS), often without users directly reading documentation, reporting bugs, or otherwise engaging with maintainers. We study the equilibrium effects of vibe coding on the OSS ecosystem. We develop a model with endogenous entry and heterogeneous project quality in which OSS is a scalable input into producing more software. Users choose whether to use OSS directly or through vibe coding. Vibe coding raises productivity by lowering the cost of using and building on existing code, but it also weakens the user engagement through which many maintainers earn returns. When OSS is monetized only through direct user engagement, greater adoption of vibe coding lowers entry and sharing, reduces the availability and quality of OSS, and reduces welfare despite higher productivity. Sustaining OSS at its current scale under widespread vibe coding requires major changes in how maintainers are paid…(More)”.

Article by Tina Chakrabarty: “…Intelligent agents—autonomous software entities that can learn, act and collaborate to maintain and enhance data ecosystems—are shaping the next frontier of enterprise data. These agents can:

• Continuously scan datasets for drift, bias and integrity issues.
• Auto-classify data based on use-case sensitivity.
• Generate enriched metadata and context.
• Recommend access controls based on behavioral patterns.
• Validate whether data is fit for use for LLMs.
• Trigger alerts and remediation without human intervention.

Instead of humans managing data tasks manually, agents become active co-pilots—ensuring that every data element is AI-ready. This shift from passive governance to proactive enablement is already transforming how AI models scale globally…(More)”.

Paper by Andreas P. Distel, Christoph Grimpe, and Marion Poetz: “We examine the use of scientific research in the development of policy documents within the context of clinical practice guidelines (CPGs) for diagnosing, treating, and managing diabetes. Using natural language processing, we identify “hidden citations” (i.e., textual credit without formal citations) and “token citations” (i.e., formal citations without textual credit) to scientific research within CPGs to understand how scientific evidence is selected and integrated. We find that both types of citations are pervasive, calling into question the use of formal citations alone in understanding the societal impact of scientific research. Using data on scholarly citations and expert ratings, we find that hidden citations are positively associated with the actual impact of the research on patients and caregivers while token citations associate positively with scientific impact. Qualitative insights gathered from interviews with senior guideline writers further illustrate the reasons for certain functions of scientific research, which involve balancing scientific rigor with practical demands in the guideline writing process, the need for local adaptations, political dynamics on the organizational level, and individual preferences towards certain types of studies or the use of experiential knowledge. Our work underscores the critical role of research utilization in translating scientific evidence into policy, showing that policymaker decisions shape societal impact as much as the engagement efforts of scientists, and extends institutional accounts of symbolic and substantive knowledge use…(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)”.

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)”.

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)”