Stefaan Verhulst
Report by Trevor Potts: “Artificial Intelligence (AI) is rapidly reshaping how governments gather evidence, analyze information, and make decisions. In recent years, the Government of Canada has rapidly deployed new AI technologies and tools across the federal public service. While new technologies ostensibly promise improved efficiency and productivity, their deployment hasraised serious questions around perceived lack of transparency, gaps in regulation, implementation challenges, and potential for misuse and biased decision-making.
This project aims to explore how AI is reshaping evidence usage in the federal public service, and the implications AI usage has on federal policymaking and public trust in Canadian democracy…(More)”.
Article by Matt Prewitt and Salomé Viljoen: “AI companies’ need for large tracts of land has, for the first time, given communities a real public forum to push back against Big Tech. But stopping data centers won’t be enough. We must now extend the politics this moment has unleashed and give communities greater power over the future of a technology that will transform them.
AI models inherently concentrate power. They draw information from people and communities everywhere, and give their wielders power to fragment, shape, and manipulate those same communities. They are becoming extraordinarily powerful. Even if they aren’t existentially dangerous, they are not a “normal technology”.
Lawmakers must therefore do their job and regulate AI. A full response to AI’s harms will likely include a range of proposals. We believe one essential component of AI regulation is to move past interventions aimed at harm reduction to mechanisms of redistributing power over setting and directing AI’s future. In a new proposal, we argue for creating a new class of rights governing data use and collection, which makes associations — inspired by the historical structure of guilds, unions, and cooperatives — the wielders of a new class of data rights. These data associations can become the critical counterparties with whom tech companies must negotiate to build their powerful new technologies. We call this approach “associational data rights”.
This proposal aims not at “what” to fix about AI, but “who” to empower over the direction AI development takes. Our proposal creates new legal interests in data, which individuals exercise by assigning them to voluntary, member-governed associations. These associations would then work for their interests on their behalf. Large technology companies would have to negotiate with one or several of these associations in order to get rights to use data, or avoid serious liability.
Central to these new rights is the idea that individuals should be allowed to join and form a new type of special non-stock organization called a Data Rights Association (DRA). Being a member of a DRA would communicate to technology companies that certain rights concerning your data — the right to reasonable privacy, the right to a say in how the information is used, and the right to a fair share of the economic benefits — have been assigned to the DRA as your proxy. Companies would not be allowed to discriminate against DRA members in their service provision, or require terms of service that preclude DRA membership. In a nutshell, companies seeking to use customer data at scale would need to come to terms with a handful of consumer-side organizations with real bargaining power, instead of imposing lopsided terms on divided-and-conquered individuals…(More)”.
Book by Julian Posada: “Concealed behind digital platforms, a vast, dispersed, and largely invisible workforce quietly generates and annotates the data that powers today’s AI boom. In Platform Extractivism, information scientist Julián Posada argues that these platforms are engines of extraction rooted in the enduring social inequalities and transnational power disparities of coloniality. Posada reveals that technology, especially today’s so-called AI, is not artificial, autonomous, or intelligent; it inherently relies on and extracts from humanity. Drawing on mixed-methods research on three platforms in the Venezuelan data work sector, Posada exposes the human cost of this technology, revealing how digital platforms have capitalized on economic instability, targeting vulnerable populations to extract value from their precarious labor.
A critical intervention in the debate on the future of work, this book provides profound insight into the implications of artificial intelligence and moves beyond the context of advanced economies to focus on the labor involved in its production. Platform Extractivism questions whether AI is a tool for freedom or a motor driving the widening the gap between the unseen workers who teach machines and the corporations that profit from them…(More)”.
Book by Richard Cooke: “One of the keystone inventions of the twenty-first century, Wikipedia is the largest repository of human knowledge ever made, with editions in 343 languages, read by millions of people every day. Yet, its rightful status as a “digital wonder of the world” is coupled with an odd complacency. We all use Wikipedia (often without realizing it) but seldom think about what it is and where it came from. In this entertaining book, Richard Cooke offers a fresh and definitive account of this era-defining phenomenon.
The Last Best Place on the Internet reveals the personality clashes, philosophical disputes, private obsessions, pioneering innovations, and unexpected ideas behind Wikipedia’s fabled creation, extraordinary growth, and unparalleled success. Cooke examines the profound effect of an “encyclopedia anyone can edit” on politics, business, literature, and society at large. He explores the site’s shifting role in establishing fact amid deep uncertainty about truth and authority, the often hilariously fierce debates between “Inclusionists” and “Deletionists” about what matters, and the great perils Wikipedia faces today from bots, political division, and artificial intelligence…(More)”.
Paper by Olivera Kotevska et al: “Scientific artificial intelligence (AI), spanning foundation models (FMs) to federated data-analysis pipelines, is becoming shared infrastructure across national laboratories, universities, hospitals, and industrial partners. This collaboration creates privacy risks whose natural unit is often an institution’s participation, research strategy, or technical capability rather than a single record. Differential privacy (DP), federated learning (FL), secure computation, trusted execution, and provenance each protect parts of the stack, but their guarantees rarely compose across mixed-trust institutions, access tiers, and autonomous agents. This perspective recasts privacy for scientific AI as an assurance problem defined by six elements: protected asset, observer, channel, permitted disclosure, guarantee, and evidence. We demonstrate the framing through a claim register for a composite cross-institutional scenario and use it to assess the model lifecycle. Two of the resulting gaps are specific to leadership-class facilities: scheduler, allocation, and telemetry metadata expose an institution’s resource posture, and instrument-attached control loops leak research strategy through timing and contention on shared accelerators. We identify six research priorities: institution-level guarantees, agent-communication privacy, cross-tier information flow, privacy-compatible reproducibility, leadership-scale accounting, and instrument side channels. The contribution is a common form for stating, comparing, and auditing claims whose guarantees otherwise remain fragmented across the scientific AI stack…(More)”.
Article by Gallup: “Five measures of trust in the federal government are at low points in Gallup’s trends. One-third or less of U.S. adults have a great deal or fair amount of trust in the executive (33%) and legislative branches (28%) and the federal government’s ability to handle both international and domestic problems (both at 33%). More, 46%, trust the judicial branch, but its trust has never been lower than now.
These results are from Gallup’s annual Governance poll, conducted Sept. 1-17. Gallup has asked Americans about trust in government institutions since 1972, including every year since 2000.
Public trust in government has been trending downward for the past two decades, but this year brings steeper declines in some measures. Trust in the federal government’s ability to handle international problems is down 12 percentage points from last year, and trust in the executive branch is down eight points. Trust in the federal government to deal with domestic problems is five points below last year’s level.
The drop in trust in the executive branch leaves it seven points below its prior low of 40%, recorded twice. The first was in 1974 during the Watergate era that ultimately led to President Richard Nixon’s resignation from office. The second was in 2024, after President Joe Biden ended his reelection campaign following a poor performance in a presidential candidate debate.
Meanwhile, trust in the government’s ability to handle international problems is six points below its prior low (39% in 2021), after the United States’ troubled military withdrawal from Afghanistan…(More)”.
Book by Melinda Baldwin: “The origins of the modern peer review system at both scientific journals and funding bodies—and how young peer review as a practice actually is.
In Referees We Trust? investigates the origins of the peer review system, a system that is often considered the essential mechanism that protects the trustworthiness of scientific research. Melinda Baldwin traces the first refereeing systems to journals affiliated with scientific societies in the early nineteenth century and follows refereeing as it spread from Britain to the United States. She finds that the refereeing system was not immediately embraced by all scientific journals; many institutions deliberately eschewed refereeing and were not seen as less reliable because of that choice.
The book shows that the modern link between peer review and scientific legitimacy was forged during the Cold War, as legislators and other government stakeholders began to question the need for scientists’ advice on how to award federal grant money. Scientists argued that if grant money was awarded without their expert opinions—in other words, without review by a grant applicant’s scientific peers—the US government would be violating a core principle of science itself. That argument was so successful that the idea of “peer review” as a cornerstone of science took hold and spread, elevating peer review from an optional process to a system intended to ensure the quality and trustworthiness of all of science…(More)”.
Paper by Grace Cooper and Kate Long: “Deliberative mini-publics have become an increasingly prominent feature of climate governance across advanced democracies. In the United Kingdom, their rapid expansion has been accompanied by the emergence of a professional deliberative field composed of facilitators, recruitment agencies, advisory bodies, expert witnesses, and network organizations. While existing scholarship debates whether professionalization enhances deliberative quality or introduces risks of marketization and elite capture, there is limited empirical evidence on how influence, standards, and authority are structured within this field. This article addresses that gap by mapping the actors involved in the design and delivery of UK climate mini-publics using qualitative social network analysis. The analysis examines how professional ties shape the circulation of expertise, standards, and backstage decision-making. The findings identify important and influential actors within the network, and their place within the wider industry. Here, we highlight the unequal concentration and clustering within the network. Rather than finding clear evidence of elite capture, the analysis reveals a form of structural dependency. Responsibility and authority are frequently transferred from commissioning bodies to facilitators, concentrating influence in a small number of actors. While these actors often act as safeguards of deliberative integrity, this reliance also introduces fragility, risks of stagnation, and uneven standards across the field. The article concludes that professionalization is neither inherently corrosive nor unproblematic. By foregrounding the networked architecture of deliberation, the study contributes a new perspective on how democratic innovations are governed, stabilized, and constrained in practice…(More)”.
Article by Richard Youngs: “…Amid much fretful worrying over the fragility of European democracy, citizen assemblies’ move to center stage is clearly a positive and promising development. However, all this success begs an obvious and uncomfortable question. As participative forums have inched toward mainstream status, democracy’s problems have continued to mount. Participative forums are now widespread, and yet a sense of democratic malaise persists and, if anything, has worsened.
So, has the mountain of democratic participation been scaled only for a view of future political renewal to remain obscured? Cruelly put, after so many years of hype about citizen assemblies and countless books and reports pushing them forward, has their multiplication ultimately failed to generate much democratic improvement?
A skeptical judgment would see the surge in citizen assemblies as being of limited political significance. As their numbers have increased, democracy scores have stagnated in many places or even worsened. In most European countries where assemblies have been numerous, the far right is on the march and gaining support. The limits to and doubts about mini deliberation have fallen one by one, and yet the results have been negligible. The current enthusiasm over using AI in deliberation is small matter compared with the so-called tech barons’ destruction of basic freedom and human agency worldwide.
A downbeat line on the spread of deliberative initiatives would be that assemblies are beneficial individually on specific topics but that it was naive ever to think they could help shore up democracy in any systemic sense. The implication is that it is better to let community-rooted assemblies do their work on prosaic issues and not overburden them with breathless talk and expectation about random selection replacing the traditional structures of electoral democracy…(More)”.
Article by Dean Jackson: “On a September day in 2024, Dario Amodei, CEO of Anthropic, sat on stage at the Amazon Web Service’s Public Sector Summit. His message: “AI needs to empower democracies and allow them to function better and remain competitive on the world stage.”
At a time when much of the conversation about AI and democracy still revolves around deepfakes and synthetic fakery, Amodei’s statement tapped an alternate vein. What if artificial intelligence could supplement democratic processes and institutions, lowering barriers to participation and making them more inclusive and representative?
It’s not an idle question; researchers and entrepreneurs are already designing tools and products with this premise in mind. Deployed thoughtfully, they just might deliver. If adopted carelessly, though, we could be on the verge of a civic-tech cash grab that harms where it promised to heal.
The Promise of AI-Facilitated Deliberation
It’s not just Amodei. Just days before his remarks, 23 scholars released a paper titled “How Will Advanced AI Systems Impact Democracy?” Their expectations are not rose-tinted; they warn of both opportunity and the potential for serious disruption of democratic processes and institutions.
The key throughline is language, the domain in which large language models (LLMs) excel. “Language,” the authors write,
allows information about candidates and policies to be shared with to [sic] voters, lawmakers to create legislation, and citizens and representatives to collectively discuss, deliberate and decide on which course of action to pursue. In liberal democracies, elected leaders use oratory to explain and justify their decisions and actions to the larger public, interest groups use persuasive messaging to lobby for their preferred policies, and the general public engages in debate, either informally or through organized events such as town halls and citizens’ assemblies.
These forms of communication and, more crucially, deliberation are the stuff of democracy. Research from cognitive science tells us that groups make better decisions when they deliberate. Research from political science shows that the process of deliberation doesn’t just change the decisions people make, it also changes how they feel about the deliberative outcome and one another.
Given this understanding, the authors’ recommendations also make sense. They write about “bridging algorithms” to encourage constructive dialogue rather than online conflict. They also refer to “new avenues” through which government officials and campaigning politicians can gain insight into the public’s views and concerns.
Major voices in the tech industry are especially bullish on this latter concept. In a recent essay in the MIT Technology Review, Andrew Sorota and Josh Handler—who work on AI policy for Eric Schmidt, former CEO and chairman of Google—note that “several states and localities are already using AI-mediated platforms to conduct democratic deliberation at scale, building on research showing that AI mediators can help citizens find common ground.”…(More)”.