Stefaan Verhulst
Paper by Lorenzo Manuali: “An increasingly large number of projects seek to use Large Language Models (LLMs) to enhance or support democracy. I argue that many of the ways in which computer scientists, deliberative democratic practitioners, and others are using LLMs to do this threatens the non-instrumental value of a collective’s ability to determine its own future. In particular, I present a novel worry that projects aimed at algorithmically facilitating deliberation and representing people’s interests in political processes threaten what has been called democratic autonomy. I begin with some conceptual groundwork concerning collective self-determination (and specifically democratic autonomy) to motivate its non-instrumental value. Next, I offer a few necessary conditions for democratic autonomy from the literature, such as the possession of a joint intention and said joint intention being realized in policy at least some significant portion of the time. I then show when two kinds of projects that use LLMs to enhance democracy – facilitative and representative LLMs – threaten these necessary conditions and thus democratic autonomy. I conclude by outlining some practical upshots and recommendations for projects that aim to use LLMs to enhance or support democracy…(More)”.
Paper by Fergus Green and Michele Zadra: “Meeting the goals of the Paris Agreement on climate change demands rapid and systemic economic transformation. Yet, contemporary democracies seem tragically ill-suited to this task. Proposals to insulate climate policy making from democratic control—for instance, through delegation to expert bodies—seem likely to fuel the rise of authoritarian populists, which is already eroding democratic norms and institutions. Deepening democratic engagement with the process of decarbonization may provide an escape route from this apparent “democracy–decarbonization dilemma.” In this vein, there has recently been much enthusiasm for climate assemblies. Such processes can have profound effects on their participants. But can they influence attitudes to democracy and support for ambitious climate policy among the wider public? We conduct a narrative review of the theoretical and empirical literature on democratic mini-publics (DMPs) to answer this question and critically reflect on our findings. We find that DMPs can influence the wider public’s attitudes toward democratic institutions and public policy to some extent. Yet, DMPs alone—even if well publicized—seem unlikely to be sufficient to stimulate the widespread shifts in knowledge, capacities, attitudes and behavior necessary to escape the democracy–decarbonization dilemma. We propose expanding the use, institutionalization, and orientation of DMPs—so that they become a widely recognized and routinized feature of deliberative systems, interacting with both governments and the wider citizenry—while advancing complementary democratic reform initiatives…(More)”.
Report by the Tony Blair Institute: “Strong political leadership has always played a decisive role in determining which states succeed and which do not. This is especially true during times of disruption. Decisions made during these periods are often urgent and based on limited information, and their outcomes can have long-lasting effects on a country’s future trajectory.
Today, every leader is facing this scenario. Artificial intelligence will reshape every aspect of our world, from how wars are fought and labour markets operate to how science and research are conducted. Capabilities at the frontier are advancing almost weekly. Leaders are therefore confronting a technology whose consequences will extend across almost every area of government.
Yet while AI’s potential is widely understood, how it will evolve is not. Leaders know they need to act, but what to do and how much to do are often less clear. They must decide how much energy and compute to make available, how to prepare their economies and workforces, how to secure increasingly digital states, and how far to intervene in the development and deployment of the technology. In the West, they must make these choices amid a growing “techlash” against AI – but while doing less might help politically in the short term, it may carry significant long-term impacts on a country’s stability and security.
Political leaders cannot navigate this alone and are highly dependent on the institutions that support them, both to provide the evidence and expertise needed to understand the issues and potential decisions, and to implement those decisions. Yet the challenges that leaders face today often require multiple institutions, departments or regulators to work together, drawing on different strands of evidence and operating under different regulations. This increases the complexity of coordination and delivery. Leaders’ ability to navigate the age of AI will therefore depend on both the quality of the choices they make and on the capacity of the institutions around them to inform and implement those choices.
Technology, and especially AI, will be key to transforming how a state’s institutions work and improving their capacity to deliver. AI can help by enabling government to synthesise information across institutional boundaries, retrieve and apply institutional knowledge, and sustain coordination around shared outcomes. But technology alone is not enough. A new operating model is also needed that can combine political decision-making and authority with institutional expertise and is supported by abundant machine intelligence. Done well, this would give leaders greater capacity to understand the choices before them – and their potential consequences – while giving institutions greater capacity to translate those decisions into action…(More)”.
Article by Zeynep Engin, Jon Crowcroft and Stefaan Verhulst: “Academic peer review is in crisis—it is a structural reality that every editor, reviewer, and conscientious author now navigates daily. The symptoms are familiar: reviewer fatigue, inconsistent decisions, declining response rates, and a growing sense that the machinery of scholarly quality assurance is straining under a load it was never designed to bear. For a journal like Data & Policy—operating at the intersection of multiple disciplinary traditions and sectorial experiences, serving a field still in the process of constituting itself—these pressures are not abstract. They shape every editorial decision we make…(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)”.
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)”.