Stefaan Verhulst

Article by BKReader: “New York City Public Advocate Jumaane D. Williams released a new report titled “Artificially Inevitable,” examining the role of artificial intelligence local, state and federal government, and outlining guidelines for responsible use of the rapidly developing technology.

According to the report, power consumption by data centers in the United States are estimated to drive almost half the growth in electrical demand within the next 5 years. The nation’s largest data centers require up to 5 million gallons of water a day, the same water use for a town with between 10,000 to 50,000 residents. 

“Our government has a responsibility to help New Yorkers better understand this technology that is rapidly changing the world we live in, and to ensure that when this technology is used, it is to the benefit of New Yorkers,” said Williams upon the report’s release…(More)”.

Article by Jamie Gibbon: “With demand for seafood rising worldwide and ocean health facing an array of threats, ensuring the sustainability of fisheries has never been more critical. Aside from national governments, that responsibility falls mostly to regional fisheries management organizations (RFMOs), which set catch limits along with rules on how, where and when fleets may fish and transfer catch on the high seas.

To guard against overfishing, RFMOs and their member countries must set those catch limits based on the best available science, monitor the activity of their fleets and work to improve compliance with the rules they have agreed upon.

Across the vast expanses of international waters, which in most cases begin 200 miles from the nearest shore, RFMOs face challenges in accomplishing those mandates. As just one example: RFMOs have long required human observer coverage on some vessels to collect critical data, but using people in this role can be dangerous and expensive…(More)”.

Article by Melissa Dell & Ashesh Rambachan: “Artificial intelligence (AI) is transforming measurement in economics. AI models convert unstructured data, such as text and images, into structured variables at low cost, making previously prohibitive measurement feasible at scale. This shifts the bottleneck from finding any scalable measure of a phenomenon to choosing among many plausible ones, which may support different empirical conclusions. This review provides guidance for navigating that shift. We describe three stages at which AI enters the measurement pipeline—discovery, construct definition, and observation—and what each demands of researchers. We argue that credible inference with AI-generated variables requires appropriately designed validation: anchoring measurement to explicit criteria, rather than informal claims that a proxy is reasonable. We then examine how validation samples support valid inference even when AI predictions are arbitrarily biased, and what can be done when a random validation sample is unavailable…(More)”.

Article by Mark Arsenault, Dana Goldstein, Alan Blinder, and Sarah Mervosh: “The worries that artificial intelligence is degrading education have been building for months. Students are using it for everything, including cheating, and a recent report out of M.I.T. said it is triggering “cognitive surrender.”

Many university leaders are embracing A.I. with enthusiasm anyway.

As A.I. leaders warn about the dangers of their own technology, the dissonance between the optimism of university leaders and the fears of many students and professors may increase. Professors and students are often in a bind about what they should do: Use more A.I. to stay relevant, or dial back to preserve learning and thinking?

While some politicians and A.I. leaders are warning about possible doomsday scenarios for humanity as A.I. models advance, many campus leaders are focused on the existential threat the technology poses to universities.

The committee at the Massachusetts Institute of Technology studying A.I. use released a number of alarming findings, including that the technology has caused “major shifts in campus culture,” isolating students who are skipping office hours and ditching communal studying. Getting answers from a bot can create “the illusion of learning” for students who reach for A.I. “at the first hint of struggle,” the M.I.T. committee wrote in its August report.

At the same time, “the potential of these technologies to augment work across campus is immense,” the committee wrote.

The M.I.T. report crystallized some of the thinking in higher education about A.I., but top administrators have been embroiled in debates for several years. Administrators have often been the most eager to champion A.I. efforts, while many faculty and students remain more skeptical…(More)”.

Article by USA Facts: “It is often said of trust that it takes years to build and seconds to destroy. Is America’s federal data infrastructure experiencing those destructive seconds?

Americans are losing trust in the government as a source of information, according to the State of the Facts poll released today by USAFacts and AP/NORC. The percentage of people who said they trust information from federal agencies dropped from 18% in 2024 to 11% in 2026. The seven-point drop was the largest among 15 information sources.

Source: USAFacts, AP/NORC

In 2024, people reported trusting federal agencies about as much as national newspapers or cable news networks. Today, fewer people report “a great deal” or “quite a bit” of trust in federal agencies than in YouTube and social media.

The same poll illustrates just why we need that information. A 43% share of respondents attribute our country’s political division to “relying on different facts” about major problems as opposed to having different beliefs. Consider that — nearly half of Americans don’t actually think we disagree ideologically on certain political issues, just that we have different information.

So how does this polarized public want to determine fact from fiction? Put simply, data. Sixty percent say they are very likely to see information as factual if it is based in data, the number one driver of confidence…(More)”.

Article by Antonio Regalado: “Last year Ruxandra Teslo, a policy analyst who focuses on clinical trials, posted an idea for supercharging medical AI systems: Use data from failed biotech companies.

By bidding at their bankruptcy proceedings, she proposed, it might be possible to obtain detailed regulatory filings, manufacturing strategies, and safety data—types of information usually considered trade secrets. She called these documents “biotech’s lost archive” and said they could be used to help train AIs that would act as powerful copilots in the often opaque drug approval process. 

Today the OpenAI Foundation, the nonprofit parent of OpenAI, said it would fund her idea as part of a new effort it calls Public Data for Health, which aims to help artificial intelligence make big leaps in medicine by paying to create “high-quality scientific datasets.”

The basic idea is that AI isn’t going to be capable of making important breakthroughs in curing disease unless researchers can feed the models much more information than they have so far…(More)”.

Launch by the Ethical Data Initiative: “The Munich Manifesto for Equitable Open Research originated within the Philosophy of Open Science for Diverse Research Environments (PHIL_OS) project, led by Professor Sabina Leonelli at the Technical University of Munich (TUM) as part of a European Research Council (ERC) project. …

While open publishing and shared datasets have accelerated discovery, they have also created new hurdles. Top-down policies and standardised digital tools are usually built around well-funded institutions. As a result, wealthy research centres end up setting the standards, while researchers in less-resourced settings, non-academic experts, and local knowledge holders can be left behind.

Furthermore, unchecked commercial practices, such as high open-access publishing fees and uncredited AI scraping of public data, threaten to widen global disparities rather than close them.

Read and support the Munich Manifesto here

The Munich Manifesto directly tackles these systemic risks across five key areas:

• Diversity: Respecting local knowledge, domain-specific methods, and distinct research traditions.

• Resilience: Supporting long-term stewardship for both digital and physical research infrastructures.

• Engagement: Building genuine, non-exploitative partnerships across communities.

• Justice: Addressing historical inequities and offering fair recognition for all contributions.

• Honesty: Remaining transparent about system limitations, security risks, and biases…(More)”.

Introduction by Bill Anderson: “Digital transformations across Africa are becoming increasingly difficult to keep track of. Firstly because there is a lot going on. Secondly because there is a lot more information available. Thirdly because the dividing line in the narrative between bad news and good news is complex. Corpus is an attempt to overcome some of the obstacles experienced by researchers, analysts and writers across Africa in maintaining an up-to-date evidence base. It is offered to our community as a digital public good.

corpus, noun. a large, structured collection of written or spoken texts used for research and analysis

Corpus collects publicly available news and documents on digital transformation, digital public infrastructures and data governance covering the whole of Africa. As of today:

• It contains over 21,000 documents.
• Over 1,500 have been newly published this month.
• They are classified geographically by country and region.
• They are categorised into 38 topics.
• They are linked to 121 indicators.

It provides a suite of reports:

• A bulletin summarises yesterday’s news (updated every night).
• Monthly updates collate the daily updates for the past month.
• Country status reports assess the overall maturity of a country across the 38 topics.
• Country progress reports track developments for each of the 121 indicators over the past 12 months.
• Topic progress reports pivot the country data to report developments by country for each indicator.
• The catalogue allows you to filter and download a reading list for your specific purposes.
• Financial reports monitor all non-state investments. (A separate report on national budgets and expenditure is still under development.)…(More)”.

Essay by Amelia Acker: “…When I teach my students the significance of cloud infrastructure in contemporary information technology, we usually start with a much older information network—the U.S. Postal Service (USPS). Just like our digital data today, a mailed letter is accepted, sorted, and stored, then routed and transmitted across a physical network for delivery. Each of these information processes that support mail delivery requires physical facilities, technical standards, security practices, trained workers, and policy about who gets served and under what conditions. Since the Postal Service Act of 1792, federal policy has attached public obligations to those processes because Americans came to depend on those networked services for information, news, and commerce.

Telecommunications policy has also followed a similar path. Concepts such as universal service, privacy, ensuring continuity, and reasonable access all grew out of fights over who controlled communications infrastructure and what that control required of commercial operators. Just like the USPS mail system, communication networks are critical to participating in society. The Communications Act of 1934 created the Federal Communications Commission to make, as possible, wire and radio communication available to all the people of the U.S. with adequate facilities at reasonable charges. The Telecommunications Act of 1996 later codified universal service formally in Section 254.

The stack of IT services we call “the cloud” is a lot like what the USPS does with our mail. The cloud stores information, moves it across locations, authenticates users, routes and verifies requests, runs applications, and determines who can access data and how. But importantly, storage and transmission increasingly happen inside the stack in ways that make users dependent on these services. This drift towards enclosure and dependency on cloud computing services is sometimes called the generative entrenchment of platforms. A teacher’s entire course can be built in Canvas, a legal contract is shared in Microsoft Outlook and kept in OneDrive, a patient’s medical history is stored in a healthcare records portal. Each is incredibly convenient, but they are all vulnerable to system-wide outages, and portability options vary, making it harder to leave the longer you stay.  

The history of postal and telecommunications policy shows that once an intermediary becomes critical to communication, society begins to build expectations around it and attach obligations to its position in the network as a public good. In his book The Master Switch: The Rise and Fall of Information Empires, legal scholar Tim Wu analyzes the Telecommunications Act of 1996 as part of a recurring historical cycle where open communications systems inevitably consolidate into centralized monopolies that policy needs to interrupt. A similar pattern is now happening with “hyperscalers” like Amazon, Microsoft, and Google, as these firms are accumulating market dominance like telecommunications firms once held.

For infrastructure studies scholars like Ryan Ellis, histories of critical infrastructure—such as telecommunications networks and the electrical grid—show how security crises and outages can bring together publics—workers, corporations, regulators, communities, and governments—whose interests become bound in a shared system. These “infrastructure publics” relate to one another, each with competing interests as they face crises, technology adoption, and regulatory efforts, among other social forces. The global Canvas software hack is a case in point. The software outage showed what securing infrastructure means on the ground and whose interests should receive priority when a shared network system fails its users…(More)”

Paper by Elena Murray, Moiz Raja Shaikh, Stefaan Verhulst, Hinali Doshi, Romeo Leapciuc, Perizat Mamutalieva and Mahadia Tunga: “As data-driven service delivery expands, data reuse holds significant potential to improve access to and quality of essential services for young people. However, limited youth involvement in decisions about how their data is reused risks perpetuating mistrust and deepening the inequalities that these services seek to address, particularly if young people choose to avoid seeking services or withhold critical information out of fear of misuse. Grounded in a social license approach, responsible data reuse aimed at enhancing service delivery therefore requires methodologies that meaningfully engage youth and reflect their preferences and expectations. This article presents findings from the NextGenData project, which developed and piloted a scalable methodology for engaging young people aged 19–24 in co-designing responsible data reuse strategies. Conducted as a year-long participatory action research initiative across India, Tanzania, Moldova, and Kyrgyzstan, the approach implemented youth assemblies, deliberative methods, and localised facilitation by national partners to engage young people. Through a cross-contextual analysis, this study emphasises the importance of context-sensitive, multi-phase engagement in supporting the development of a social license for data reuse and presents a publicly available toolkit designed to support the replication and adaptation of this engagement strategy in diverse contexts. Drawing on the findings, recommendations are presented for policymakers and practitioners to guide future initiatives…(More)”.