Paper by Sachit Mahajan and Dirk Helbing: “As artificial intelligence (AI) systems increasingly shape everyday life, integrating diverse community values into their development becomes both an ethical imperative and a practical necessity. This paper introduces Value Sensitive Citizen Science (VSCS), a systematic framework combining Value Sensitive Design (VSD) principles with citizen science methods to foster meaningful public participation in AI. Addressing critical gaps in existing approaches, VSCS integrates culturally grounded participatory methods and structured cognitive scaffolding through the Participatory Value-Cognition Taxonomy (PVCT). Through iterative value-sensitive participation cycles guided by an extended scenario logic (What-if, If-then, Then-what, What-now), community members act as genuine coresearchers-identifying, translating, and operationalizing local values into concrete technical requirements. The framework also institutionalizes governance structures for ongoing oversight, adaptability, and accountability across the AI lifecycle. By explicitly bridging participatory design with algorithmic accountability, VSCS ensures that AI systems reflect evolving community priorities rather than reinforcing top-down or monocultural perspectives. Critical discussions highlight VSCS’s practical implications, addressing challenges such as power dynamics, scalability, and epistemic justice. The paper concludes by outlining actionable strategies for policymakers and practitioners, alongside future research directions aimed at advancing participatory, value-driven AI development across diverse technical and sociocultural contexts…(More)”.
Technical Tiers: A New Classification Framework for Global AI Workforce Analysis
Report by Siddhi Pal, Catherine Schneider and Ruggero Marino Lazzaroni: “… introduces a novel three-tiered classification system for global AI talent that addresses significant methodological limitations in existing workforce analyses, by distinguishing between different skill categories within the existing AI talent pool. By distinguishing between non-technical roles (Category 0), technical software development (Category 1), and advanced deep learning specialization (Category 2), our framework enables precise examination of AI workforce dynamics at a pivotal moment in global AI policy.
Through our analysis of a sample of 1.6 million individuals in the AI talent pool across 31 countries, we’ve uncovered clear patterns in technical talent distribution that significantly impact Europe’s AI ambitions. Asian nations hold an advantage in specialized AI expertise, with South Korea (27%), Israel (23%), and Japan (20%) maintaining the highest proportions of Category 2 talent. Within Europe, Poland and Germany stand out as leaders in specialized AI talent. This may be connected to their initiatives to attract tech companies and investments in elite research institutions, though further research is needed to confirm these relationships.
Our data also reveals a shifting landscape of global talent flows. Research shows that countries employing points-based immigration systems attract 1.5 times more high-skilled migrants than those using demand-led approaches. This finding takes on new significance in light of recent geopolitical developments affecting scientific research globally. As restrictive policies and funding cuts create uncertainty for researchers in the United States, one of the big destinations for European AI talent, the way nations position their regulatory environments, scientific freedoms, and research infrastructure will increasingly determine their ability to attract and retain specialized AI talent.
The gender analysis in our study illuminates another dimension of competitive advantage. Contrary to the overall AI talent pool, EU countries lead in female representation in highly technical roles (Category 2), occupying seven of the top ten global rankings. Finland, Czechia, and Italy have the highest proportion of female representation in Category 2 roles globally (39%, 31%, and 28%, respectively). This gender diversity represents not merely a social achievement but a potential strategic asset in AI innovation, particularly as global coalitions increasingly emphasize the importance of diverse perspectives in AI development…(More)”
Mini-Publics and Party Ideology: Who Commissioned the Deliberative Wave in Europe?
Paper by Rodrigo Ramis-Moyano et al: “The increasing implementation of deliberative mini-publics (DMPs) such as Citizens’ Assemblies and Citizens’ Juries led the OECD to identify a ‘deliberative wave’. The burgeoning scholarship on DMPs has increased understanding of how they operate and their impact, but less attention has been paid to the drivers behind this diffusion. Existing research on democratic innovations has underlined the role of the governing party’s ideology as a relevant variable in the study of the adoption of other procedures such as participatory budgeting, placing left-wing parties as a prominent actor in this process. Unlike this previous literature, we have little understanding of whether mini-publics appeal equally across the ideological spectrum. This paper draws on the large-N OECD database to analyse the impact of governing party affiliation on the commissioning of DMPs in Europe across the last four decades. Our analysis finds the ideological pattern of adoption is less clear cut compared to other democratic innovations such as participatory budgeting. But stronger ideological differentiation emerges when we pay close attention to the design features of DMPs implemented…(More)”.
Hundreds of scholars say U.S. is swiftly heading toward authoritarianism
Article by Frank Langfitt: “A survey of more than 500 political scientists finds that the vast majority think the United States is moving swiftly from liberal democracy toward some form of authoritarianism.
In the benchmark survey, known as Bright Line Watch, U.S.-based professors rate the performance of American democracy on a scale from zero (complete dictatorship) to 100 (perfect democracy). After President Trump’s election in November, scholars gave American democracy a rating of 67. Several weeks into Trump’s second term, that figure plummeted to 55.
“That’s a precipitous drop,” says John Carey, a professor of government at Dartmouth and co-director of Bright Line Watch. “There’s certainly consensus: We’re moving in the wrong direction.”…Not all political scientists view Trump with alarm, but many like Carey who focus on democracy and authoritarianism are deeply troubled by Trump’s attempts to expand executive power over his first several months in office.
“We’ve slid into some form of authoritarianism,” says Steven Levitsky, a professor of government at Harvard, and co-author of How Democracies Die. “It is relatively mild compared to some others. It is certainly reversible, but we are no longer living in a liberal democracy.”…Kim Lane Scheppele, a Princeton sociologist who has spent years tracking Hungary, is also deeply concerned: “We are on a very fast slide into what’s called competitive authoritarianism.”
When these scholars use the term “authoritarianism,” they aren’t talking about a system like China’s, a one-party state with no meaningful elections. Instead, they are referring to something called “competitive authoritarianism,” the kind scholars say they see in countries such as Hungary and Turkey.
In a competitive authoritarian system, a leader comes to power democratically and then erodes the system of checks and balances. Typically, the executive fills the civil service and key appointments — including the prosecutor’s office and judiciary — with loyalists. He or she then attacks the media, universities and nongovernmental organizations to blunt public criticism and tilt the electoral playing field in the ruling party’s favor…(More)”.
Test and learn: a playbook for mission-driven government
Playbook by the Behavioral Insights Team: “…sets out more detailed considerations around embedding test and learn in government, along with a broader range of methods that can be used at different stages of the innovation cycle. These can be combined flexibly, depending on the stage of the policy or service cycle, the available resources, and the nature of the challenge – whether that’s improving services, testing creative new approaches, or navigating uncertainty in new policy areas.
Almost all of the methods set out can be augmented or accelerated by harnessing AI tools – from using AI agents to conduct large-scale qualitative research, to AI-enhanced evidence discovery and analysis, and AI-powered systems mapping and modelling. AI should be treated as a core component of the toolkit at each stage. And the speed of evolution of the application of AI is another strong argument for maintaining an agile mindset and regularly updating our ways of working.
We hope this playbook will make test-and-learn more tangible to people who are new to it, and will expand the toolkit of people who have more experience with the approach. And ultimately we hope it will serve as a practical cheatsheet for building and improving the fabric of life…(More)”.
AI models could help negotiators secure peace deals
The Economist: “In a messy age of grinding wars and multiplying tariffs, negotiators are as busy as the stakes are high. Alliances are shifting and political leaders are adjusting—if not reversing—positions. The resulting tumult is giving even seasoned negotiators trouble keeping up with their superiors back home. Artificial-intelligence (AI) models may be able to lend a hand.
Some such models are already under development. One of the most advanced projects, dubbed Strategic Headwinds, aims to help Western diplomats in talks on Ukraine. Work began during the Biden administration in America, with officials on the White House’s National Security Council (NSC) offering guidance to the Centre for Strategic and International Studies (CSIS), a think-tank in Washington that runs the project. With peace talks under way, CSIS has speeded up its effort. Other outfits are doing similar work.
The CSIS programme is led by a unit called the Futures Lab. This team developed an AI language model using software from Scale AI, a firm based in San Francisco, and unique training data. The lab designed a tabletop strategy game called “Hetman’s Shadow” in which Russia, Ukraine and their allies hammer out deals. Data from 45 experts who played the game were fed into the model. So were media analyses of issues at stake in the Russia-Ukraine war, as well as answers provided by specialists to a questionnaire about the relative values of potential negotiation trade-offs. A database of 374 peace agreements and ceasefires was also poured in.
Thus was born, in late February, the first iteration of the Ukraine-Russia Peace Agreement Simulator. Users enter preferences for outcomes grouped under four rubrics: territory and sovereignty; security arrangements; justice and accountability; and economic conditions. The AI model then cranks out a draft agreement. The software also scores, on a scale of one to ten, the likelihood that each of its components would be satisfactory, negotiable or unacceptable to Russia, Ukraine, America and Europe. The model was provided to government negotiators from those last three territories, but a limited “dashboard” version of the software can be run online by interested members of the public…(More)”.
The Future of Health Is Preventive — If We Get Data Governance Right
Article by Stefaan Verhulst: “After a long gestation period of three years, the European Health Data Space (EHDS) is now coming into effect across the European Union, potentially ushering in a new era of health data access, interoperability, and innovation. As this ambitious initiative enters the implementation phase, it brings with it the opportunity to fundamentally reshape how health systems across Europe operate. More generally, the EHDS contains important lessons (and some cautions) for the rest of the world, suggesting how a fragmented, reactive model of healthcare may transition to one that is more integrated, proactive, and prevention-oriented.
For too long, health systems–in the EU and around the world–have been built around treating diseases rather than preventing them. Now, we have an opportunity to change that paradigm. Data, and especially the advent of AI, give us the tools to predict and intervene before illness takes hold. Data offers the potential for a system that prioritizes prevention–one where individuals receive personalized guidance to stay healthy, policymakers access real-time evidence to address risks before they escalate, and epidemics are predicted weeks in advance, enabling proactive, rapid, and highly effective responses.
But to make AI-powered preventive health care a reality, and to make the EHDS a success, we need a new data governance approach, one that would include two key components:
- The ability to reuse data collected for other purposes (e.g., mobility, retail sales, workplace trends) to improve health outcomes.
- The ability to integrate different data sources–clinical records and electronic health records (EHRS), but also environmental, social, and economic data — to build a complete picture of health risks.
In what follows, we outline some critical aspects of this new governance framework, including responsible data access and reuse (so-called secondary use), moving beyond traditional consent models to a social license for reuse, data stewardship, and the need to prioritize high-impact applications. We conclude with some specific recommendations for the EHDS, built from the preceding general discussion about the role of AI and data in preventive health…(More)”.
Fostering Open Data
Paper by Uri Y. Hacohen: “Data is often heralded as “the world’s most valuable resource,” yet its potential to benefit society remains unrealized due to systemic barriers in both public and private sectors. While open data-defined as data that is available, accessible, and usable-holds immense promise to advance open science, innovation, economic growth, and democratic values, its utilization is hindered by legal, technical, and organizational challenges. Public sector initiatives, such as U.S. and European Union open data regulations, face uneven enforcement and regulatory complexity, disproportionately affecting under-resourced stakeholders such as researchers. In the private sector, companies prioritize commercial interests and user privacy, often obstructing data openness through restrictive policies and technological barriers. This article proposes an innovative, four-layered policy framework to overcome these obstacles and foster data openness. The framework includes (1) improving open data infrastructures, (2) ensuring legal frameworks for open data, (3) incentivizing voluntary data sharing, and (4) imposing mandatory data sharing obligations. Each policy cluster is tailored to address sector-specific challenges and balance competing values such as privacy, property, and national security. Drawing from academic research and international case studies, the framework provides actionable solutions to transition from a siloed, proprietary data ecosystem to one that maximizes societal value. This comprehensive approach aims to reimagine data governance and unlock the transformative potential of open data…(More)”.
Why more AI researchers should collaborate with governments
Article by Mohamed Ibrahim: “Artificial intelligence (AI) is beginning to transform many industries, yet its use to improve public services remains limited globally. AI-based tools could streamline access to government benefits through online chatbots or automate systems by which citizens report problems such as potholes.
Currently, scholarly advances in AI are mostly confined to academic papers and conferences, rarely translating into actionable government policies or products. This means that the expertise at universities is not used to solve real-world problems. As a No10 Innovation Fellow with the UK government and a lecturer in spatial data science, I have explored the potential of AI-driven rapid prototyping in public policy.
Take Street.AI, a prototype smartphone app that I developed, which lets citizens report issues including potholes, street violence or illegal litter dumping by simply taking a picture through the app. The AI model classifies the problem automatically and alerts the relevant local authority, passing on the location and details of the issue. A key feature of the app is its on-device processing, which ensures privacy and reduces operational costs. Similar tools were tested as an early-warning system during the riots that swept the United Kingdom in July and August 2024.
AI models can also aid complex decision-making — for instance, that involved in determining where to build houses. The UK government plans to construct 1.5 million homes in the next 5 years, but planning laws require that several parameters be considered — such as proximity to schools, noise levels, the neighbourhoods’ built-up ratio and flood risk. The current strategy is to compile voluminous academic reports on viable locations, but an online dashboard powered by AI that can optimize across parameters would be much more useful to policymakers…(More)”.
AI for collective intelligence
Introduction to special issue by Christoph Riedl and David De Cremer: “AI has emerged as a transformative force in society, reshaping economies, work, and everyday life. We argue that AI can not only improve short-term productivity but can also enhance a group’s collective intelligence. Specifically, AI can be employed to enhance three elements of collective intelligence: collective memory, collective attention, and collective reasoning. This editorial reviews key emerging work in the area to suggest ways in which AI can support the socio-cognitive architecture of collective intelligence. We will then briefly introduce the articles in the “AI for Collective Intelligence” special issue…(More)”.