Stefaan Verhulst

Paper by Terence Chau et al: “Program impact estimates that are not statistically significant are often treated as providing little guidance for policy choice, even when economically important effects remain plausible. In such cases, the decision-relevant task is not simply to test whether the effect differs from zero, but to estimate the probability distribution of programmatic effects. The importance of going beyond hypothesis testing is especially clear when evaluations are underpowered relative to the effect needed for benefits to exceed costs. We illustrate this point using Chicago’s contract for ShotSpotter, an acoustical gunshot detection system that reduces police response times and may therefore have improved survival among shooting victims. Using a geographic RD design, we estimate that ShotSpotter reduced the shooting fatality rate by 2.5 percentage points, equivalent to roughly 49 fewer deaths per year. Although the estimate is not statistically significant, its implied benefit far exceeds the cost of the service. To have enough statistical power under the hypothesis testing approach to detect an effect of this size would require 87 years’ worth of Chicago shooting data. We instead implement a decision-relevant perspective using Bayesian analysis, treating the life-saving effect as a random variable. We find positive expected net benefits, with posterior probabilities that benefits exceed costs ranging from 70% under a flat prior and a $0.5 million value of statistical life to 99.6% under a no-harm prior and a $13.6 million value. The results illustrate how reliance on statistical significance can discard decision-relevant evidence when economically meaningful effects are difficult to detect precisely…(More)”.

Article by Valerie Volcovici: “…Reuters interviewed 15 major environmental, conservation and development organizations about their AI use. All said they used the technology in some form and expressed concern about the environmental impact of the data ​centers powering it. The centers, filled with servers, can require vast amounts of electricity, along with water for cooling.

It’s a global dilemma for individuals, governments and industries balancing AI’s benefits with its enormous risks. The tradeoffs likely will grab attention at ​the UN’s COP31 climate conference this month, when host Turkey will call on governments, businesses and other institutions to sign a pledge outlining principles to ensure AI is developed and deployed sustainably…

Many of the organizations that spoke to Reuters believe AI’s benefits ​currently outweigh its costs because the technology is enabling work that was previously difficult, expensive or impossible.

Kenya’s Save the Elephants, a partner of the Wildlife Conservation Network, said it is using machine learning to improve population surveys and study elephant communication.

Frank Pope, the group’s chief executive, ​said an aerial survey of Kenya’s largest protected area in 2017 generated roughly 200,000 images that took nine people nine months to review manually. AI models trained on Nvidia’s GeForce graphics platforms can now complete similar analyses in hours.

“The results have been eye-opening,” Pope said. “We found that human surveys were missing ‌about a third of ⁠the elephants, roughly half of zebras and giraffes, and three-quarters of oryx populations.”

More accurate wildlife data can help secure financing for conservation, he said.

“Ultimately, these tools make nature measurable and therefore investable,” he said. “That’s a critical transformation, if we’re going to conserve biodiversity at scale.”

At the institute founded by the late conservation icon Jane Goodall, researchers are using AI to digitize decades of field notes about chimpanzee behavior in Tanzania’s Gombe National Park.

The project was once expected to take 13-15 years because the records were written by hand using the local Kiha language and scientific shorthand.

Thanks to a tool developed with Amazon Web Services, the notes can now be scanned, translated and structured in minutes, slashing the time needed to about two years, said Lilian Pintea, the Jane Goodall Institute’s vice president of conservation science.

During her lifetime, Pintea said, Goodall ​supported the use of AI to advance conservation science, provided people ​remained firmly in control of how it was used.

“AI technology ⁠should be seen as tools which are human-centered,” Pintea said…(More)”.

Book edited by Lilian Edwards, Catalina Goanta, and Lachlan Urquhart: “The recent wave of regulatory reforms adopted by UK and EU regulators increases the complexity of Internet law as it had developed in the past decades. Recognising the ever-growing importance of artificial intelligence in today’s public discourse, the textbook extends beyond traditional legal sectoral frameworks, and explores the intersection of law, technology, and public policy to describe and critically reflect on current and emerging issues in the modern information society.

This exploration follows thematic areas of interest, such as adtech, privacy by design, predictive policing, and social media, while also providing accessible historical overviews of how policy has developed in specific fields including platform liability, copyright, or data protection, and case studies on key topics.

The companion website (bloomsbury.pub/law-policy-internet) provides multimedia resources to delve deeper into specific topics; introductory materials for readers that are unfamiliar with the EU legal system; and teaching resources…(More)”.

Paper by Riel Miller: “War recurs not because leaders fail but because the human capacity to imagine the future has been systematically narrowed to a single mode: anticipation-for-the-future (AfF), the colonising drive to design, control, and win tomorrow. This article argues that this narrowing – what the Futures Literacy Framework calls futures illiteracy – is not a deficit correctable by better planning or wider access to foresight tools, but an ontological condition: a way of being in time that reproduces the delusional agency at the root of war. Drawing on the Theory of Anticipation, Whitehead’s concept of concrescence, Bergson’s proposition that the real creates the possible, and feminist critiques of the master’s tools from Lorde and Feukeu, the article develops the distinction between AfF and anticipation-for-emergence (AfE) as a practical way of distinguishing the simultaneity of the closed and open present. It proposes that cultivating futures literacy – understood as the meta-cognitive capacity to hold both anticipatory frames simultaneously – could change the conditions of change: not a remedy for war, but as fundamental shift in the anticipatory contexts from which war draws its so far inescapable necessity…(More)”.

Book edited by Abbey K. Elder, Shannon M. Smith and Bryan Ricupero: “Open movements all center public access and broad representation and work to prevent the concentration of power governing information and its monetization. As general discussion of openness has narrowed to focus on areas like open science, data, access, and education, these individual communities have grown distanced from each other and, occasionally, their shared values.

In five sections, Open Movements: Recognizing Challenges and Building Connections aims to surface the challenges in each of these movements and how we might more effectively collaborate for sustainable change:

• Forging and Maintaining Open Communities
• Exploring the Structures that Support Open
• Improving Access Points for Open
• Navigating Choice and Engagement in Open
• Reflecting on Our Past to Build an Open Future

Open is a continually evolving environment, but the values that guide our systems and practices will be critical well into the future. Open Movements will help practitioners engage more thoughtfully in work that supports not just broad access to information but also public empowerment. This book is also available in an open access edition…(More)”.

Book by George Rzevski: “The book describes, using clear and concise language, the next generation of Artificial Intelligence (AI), which consists of swarms of hundreds of thousands of intelligent digital agents that exhibit collective intelligence greater than the sum of the intelligence of individual agents. These agents have access to domain knowledge stored in the attached ontology and do not need training to operate. Swarms of agents self-organise to adapt to environmental changes, resist hacking, and coevolve with the systems they interact with.

The author explains the basics of Swarm AI and its uses, including adaptive schedulers, pattern recognisers, semantic processors, variable geometry engineering products, and teams of space robots. They show how swarms of agents can handle operational tasks in businesses and protect nations from pandemics, financial crises, and military attacks.

The book is written for curious readers who want to understand the simple principles behind the power of the next generation of AI. It is also aimed at business executives, administrators, politicians, government leaders, regulators and policy makers, offering them insights into the new tools that will transform the organisations they lead and the world they inhabit…(More)”.

Article by Marianne Gros: “The European Commission is now using artificial intelligence to draft new laws and simplify existing ones, it said in an annual review of progress on its ongoing simplification drive.

As part of a major plan to cut red tape and administrative costs for businesses, the EU is making deep reforms to how it creates and enforces rules on everything from tech, agriculture and migration to environmental protection and taxation.

To do that, the Commission is deploying new digital tools internally to “modernise” lawmaking. The tools aim to help its staff “identify similarities across legal acts, pointing at potential overlaps, inconsistencies or duplicates, as well as opportunities for simplification,” it said in an annual simplification report presented on Wednesday.

According to the document, the Commission is also beefing up legal training and staff to make sure new rules are not overly complex.

“Our simplification agenda is more than figures in a report. It is a story unfolding every day in the offices, factories and farms across Europe,” said EU Simplification Commissioner Valdis Dombrovskis at a press conference. Small business owners are spending “less time filling in forms and more time focused on innovation and expansion,” he added.

The EU’s rulebook has been widely criticized for being overly complex and, at times, inconsistent. The Commission’s simplification drive is designed to fix this, making it easier for businesses and governments to comply with its laws by reviewing old legislation and adopting digital tools…(More)”.

Article by John Palfrey: “Sometime in the 18th century, the Enlightenment settled a contentious, centuries-old question: Where does knowledge come from? For millennia, many people had pointed to God. But the early modern period brought a new consensus, namely that knowledge comes from human scientists working at the edge of what is known, armed with observation, mathematics, experimentation, and logic.

As the historian Steven Shapin has noted, this was a social as much as an intellectual revolution. Science started rewiring our shared value system around the idea that research and rigor are virtuous. We held ourselves and our highest institutions to the standard of scientific inquiry.

No political system reflects this shift better than democracy. The ideal of popular sovereignty rests on the principle that each of us is capable of comparing candidates or policy proposals and reaching our own informed conclusion.

Today, we are on the cusp of a similar revolution. In September, one of OpenAI’s models took less than a week to solve a math problem that had stumped scientists for decades; and AI systems are already identifying novel antibiotic candidates by searching chemical spaces too vast for any research team to explore. In a growing number of fields, machines are autonomously producing new information. Humans are still posing the questions and evaluating the outputs, but the generative act—the creative leap across the unknown—is increasingly sparked by systems that do not think the way we do or really think at all.

As we saw with the Scientific Revolution, new information systems often lead to new epistemological and ethical systems. AI implicitly teaches us to value ease, speed, and outsourcing, and these values do have their place in certain contexts. No human can parse as much data as quickly as an AI can, and in sectors like medicine, speed often saves lives.

But as anyone who has watched the internet evolve in recent years knows, moving fast can mean breaking things. We cannot allow our democratic institutions to become one of those things…(More)”.

Paper by Haiwen Li et al: “As AI systems increasingly mediate human communication, identifying conditions under which they augment collective sense-making is a critical challenge. Through a randomized experiment (N=1,173 U.S. participants across 391 small-group deliberations), we investigate whether AI-guided self-reflection improves subsequent deliberative discourse. We show that engaging with a non-directive AI chatbot instructed to structure self-reflection prior to group deliberation yields significantly higher deliberation quality (measured by the Discourse Quality Index, DQI) compared to a baseline of no self-reflection (Cohen’s d=0.31) and a condition of unassisted self-reflection (d=0.27), and the positive impact is most pronounced in groups with disagreement. This improvement may be driven by more sophisticated argument justifications, greater consensus-building effort, and increased respect for counterarguments. Furthermore, after self-reflection, participants in the AI condition reported higher empathy toward dissenting peers and a deeper understanding of opposing reasoning. Beyond observed behavior, our findings are empirically relevant to open debates between political philosophy and psychology surrounding self-reflection, demonstrating that interactive AI could transform solitary thinking into productive internal reflection. Together, these results demonstrate that even simple AI-based reflective scaffolds can foster more productive group dynamics, opening new avenues for leveraging AI to augment collective intelligence…(More)”.

Essay by Stefaan Verhulst: “…Data openness remains desirable, yet in the age of AI it can also be weaponized. Below, I propose a framework that moves beyond the open-versus-closed binary, complements the FAIR principles, and advances AI-ready data governance through five dimensions of access: availability, accessibility, accommodation, affordability, and acceptability, while clarifying who is responsible for aligning them.

…In 1981, Roy Penchansky and J. William Thomas argued that access to health care is not a property of a clinic or a patient but of the fit between them. They broke that fit into five dimensions: availability, accessibility, accommodation, affordability, and acceptability. A service could exist, be nearby, and be free and still be inaccessible if it was organized around the provider rather than the patient or if the community did not trust it.

That insight has since been carried over to public health data, where the five A’s are used to show what is lost when a common dataset is discontinued. In what follows, I argue that the five A’s deserve a wider application. In particular, they can help us navigate a debate about data and AI that still treats access largely as a door: open or closed, licensed or not, scraped or blocked. Penchansky and Thomas remind us that access is a relationship, and relationships have to be negotiated and maintained.

AI changes that relationship in two ways. First, it adds a new kind of user: models and agents that consume data at a scale and speed no human analyst ever did. Second, it changes the stakes. As I have argued elsewhere, openness has been weaponised, and the defensive response is a data winter in which data have become more enclosed and more scarce. Seen through the five A’s, the data winter is not one failure but five, and they feed on each other. Each also points to a specific task that, today, too often belongs to no one. That task is the work of the data steward.

Reading the five A’s through the fourth wave of open data

In the below, I examine the five A’s through the lens of data for AI, or what we have called the fourth wave of open data. The first wave was driven by freedom of information laws, the second by open-by-default portals, and the third by purpose-driven reuse and data collaboratives. The fourth asks how data can be prepared for generative AI and how generative AI can in turn make data more usable.

That second direction (AI for data) adds nuance to the framework. AI is not only a new consumer of data that puts pressure on each of the five A’s; it is also a tool for improving them. It can help users discover datasets, generate and enrich metadata and documentation, and make access conversational so that people can ask questions of data in plain language instead of navigating portals and schemas…(More)”.

Table: The five A’s of data access in a time of AI