Paper by Nicolò Gozzi, Nicola Perra, and Alessandro Vespignani: “Characterizing the feedback linking human behavior and the transmission of infectious diseases (i.e., behavioral changes) remains a significant challenge in computational and mathematical epidemiology. Existing behavioral epidemic models often lack real-world data calibration and cross-model performance evaluation in both retrospective analysis and forecasting. In this study, we systematically compare the performance of three mechanistic behavioral epidemic models across nine geographies and two modeling tasks during the first wave of COVID-19, using various metrics. The first model, a Data-Driven Behavioral Feedback Model, incorporates behavioral changes by leveraging mobility data to capture variations in contact patterns. The second and third models are Analytical Behavioral Feedback Models, which simulate the feedback loop either through the explicit representation of different behavioral compartments within the population or by utilizing an effective nonlinear force of infection. Our results do not identify a single best model overall, as performance varies based on factors such as data availability, data quality, and the choice of performance metrics. While the Data-Driven Behavioral Feedback Model incorporates substantial real-time behavioral information, the Analytical Compartmental Behavioral Feedback Model often demonstrates superior or equivalent performance in both retrospective fitting and out-of-sample forecasts. Overall, our work offers guidance for future approaches and methodologies to better integrate behavioral changes into the modeling and projection of epidemic dynamics…(More)”.
Spaces for democracy with generative artificial intelligence: public architecture at stake
Paper by Ingrid Campo-Ruiz: “Urban space is an important infrastructure for democracy and fosters democratic engagement, such as meetings, discussions, and protests. Artificial Intelligence (AI) systems could affect democracy through urban space, for example, by breaching data privacy, hindering political equality and engagement, or manipulating information about places. This research explores the urban places that promote democratic engagement according to the outputs generated with ChatGPT-4o. This research moves beyond the dominant framework of discussions on AI and democracy as a form of spreading misinformation and fake news. Instead, it provides an innovative framework, combining architectural space as an infrastructure for democracy and the way in which generative AI tools provide a nuanced view of democracy that could potentially influence millions of people. This article presents a new conceptual framework for understanding AI for democracy from the perspective of architecture. For the first case study in Stockholm, Sweden, AI outputs were later combined with GIS maps and a theoretical framework. The research then analyzes the results obtained for Madrid, Spain, and Brussels, Belgium. This analysis provides deeper insights into the outputs obtained with AI, the places that facilitate democratic engagement and those that are overlooked, and the ensuing consequences.Results show that urban space for democratic engagement obtained with ChatGPT-4o for Stockholm is mainly composed of governmental institutions and non-governmental organizations for representative or deliberative democracy and the education of individuals in public buildings in the city centre. The results obtained with ChatGPT-40 barely reflect public open spaces, parks, or routes. They also prioritize organized rather than spontaneous engagement and do not reflect unstructured events like demonstrations, and powerful actors, such as political parties, or workers’ unions. The places listed by ChatGPT-4o for Madrid and Brussels give major prominence to private spaces like offices that house organizations with political activities. While cities offer a broad and complex array of places for democratic engagement, outputs obtained with AI can narrow users’ perspectives on their real opportunities, while perpetuating powerful agents by not making them sufficiently visible to be accountable for their actions. In conclusion, urban space is a fundamental infrastructure for democracy, and AI outputs could be a valid starting point for understanding the plethora of interactions. These outputs should be complemented with other forms of knowledge to produce a more comprehensive framework that adjusts to reality for developing AI in a democratic context. Urban space should be protected as a shared space and as an asset for societies to fully develop democracy in its multiple forms. Democracy and urban spaces influence each other and are subject to pressures from different actors including AI. AI systems should, therefore, be monitored to enhance democratic values through urban space…(More)”.
Unequal Journeys to Food Markets: Continental-Scale Evidence from Open Data in Africa
Paper by Robert Benassai-Dalmau, et al: “Food market accessibility is a critical yet underexplored dimension of food systems, particularly in low- and middle-income countries. Here, we present a continent-wide assessment of spatial food market accessibility in Africa, integrating open geospatial data from OpenStreetMap and the World Food Programme. We compare three complementary metrics: travel time to the nearest market, market availability within a 30-minute threshold, and an entropy-based measure of spatial distribution, to quantify accessibility across diverse settings. Our analysis reveals pronounced disparities: rural and economically disadvantaged populations face substantially higher travel times, limited market reach, and less spatial redundancy. These accessibility patterns align with socioeconomic stratification, as measured by the Relative Wealth Index, and moderately correlate with food insecurity levels, assessed using the Integrated Food Security Phase Classification. Overall, results suggest that access to food markets plays a relevant role in shaping food security outcomes and reflects broader geographic and economic inequalities. This framework provides a scalable, data-driven approach for identifying underserved regions and supporting equitable infrastructure planning and policy design across diverse African contexts…(More)”.
The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
Paper by Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh, Maxwell Horton, Samy Bengio, and Mehrdad Farajtabar: “Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations remain insufficiently understood. Current evaluations primarily focus on established mathematical and coding benchmarks, emphasizing final answer accuracy. However, this evaluation paradigm often suffers from data contamination and does not provide insights into the reasoning traces’ structure and quality. In this work, we systematically investigate these gaps with the help of controllable puzzle environments that allow precise manipulation of compositional complexity while maintaining consistent logical structures. This setup enables the analysis of not only final answers but also the internal reasoning traces, offering insights into how LRMs “think”. Through extensive experimentation across diverse puzzles, we show that frontier LRMs face a complete accuracy collapse beyond certain complexities. Moreover, they exhibit a counter- intuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having an adequate token budget. By comparing LRMs with their standard LLM counterparts under equivalent inference compute, we identify three performance regimes: (1) low- complexity tasks where standard models surprisingly outperform LRMs, (2) medium-complexity tasks where additional thinking in LRMs demonstrates advantage, and (3) high-complexity tasks where both models experience complete collapse. We found that LRMs have limitations in exact computation: they fail to use explicit algorithms and reason inconsistently across puzzles. We also investigate the reasoning traces in more depth, studying the patterns of explored solutions and analyzing the models’ computational behavior, shedding light on their strengths, limitations, and ultimately raising crucial questions about their true reasoning capabilities…(More)”
Assessing data governance models for smart cities: Benchmarking data governance models on the basis of European urban requirements
Paper by Yusuf Bozkurt, Alexander Rossmann, Zeeshan Pervez, and Naeem Ramzan: “Smart cities aim to improve residents’ quality of life by implementing effective services, infrastructure, and processes through information and communication technologies. However, without robust smart city data governance, much of the urban data potential remains underexploited, resulting in inefficiencies and missed opportunities for city administrations. This study addresses these challenges by establishing specific, actionable requirements for smart city data governance models, derived from expert interviews with representatives of 27 European cities. From these interviews, recurring themes emerged, such as the need for standardized data formats, clear data access guidelines, and stronger cross-departmental collaboration mechanisms. These requirements emphasize technology independence, flexibility to adapt across different urban contexts, and promoting a data-driven culture. By benchmarking existing data governance models against these newly established urban requirements, the study uncovers significant variations in their ability to address the complex, dynamic nature of smart city data systems. This study thus enhances the theoretical understanding of data governance in smart cities and provides municipal decision-makers with actionable insights for improving data governance strategies. In doing so, it directly supports the broader goals of sustainable urban development by helping improve the efficiency and effectiveness of smart city initiatives…(More)”.
Children’s Voice Privacy: First Steps And Emerging Challenges
Paper by Ajinkya Kulkarni, et al: “Children are one of the most under-represented groups in speech technologies, as well as one of the most vulnerable in terms of privacy. Despite this, anonymization techniques targeting this population have received little attention. In this study, we seek to bridge this gap, and establish a baseline for the use of voice anonymization techniques designed for adult speech when applied to children’s voices. Such an evaluation is essential, as children’s speech presents a distinct set of challenges when compared to that of adults. This study comprises three children’s datasets, six anonymization methods, and objective and subjective utility metrics for evaluation. Our results show that existing systems for adults are still able to protect children’s voice privacy, but suffer from much higher utility degradation. In addition, our subjective study displays the challenges of automatic evaluation methods for speech quality in children’s speech, highlighting the need for further research…(More)”. See also: Responsible Data for Children.
Collective Bargaining in the Information Economy Can Address AI-Driven Power Concentration
Position paper by Nicholas Vincent, Matthew Prewitt and Hanlin Li: “…argues that there is an urgent need to restructure markets for the information that goes into AI systems. Specifically, producers of information goods (such as journalists, researchers, and creative professionals) need to be able to collectively bargain with AI product builders in order to receive reasonable terms and a sustainable return on the informational value they contribute. We argue that without increased market coordination or collective bargaining on the side of these primary information producers, AI will exacerbate a large-scale “information market failure” that will lead not only to undesirable concentration of capital, but also to a potential “ecological collapse” in the informational commons. On the other hand, collective bargaining in the information economy can create market frictions and aligned incentives necessary for a pro-social, sustainable AI future. We provide concrete actions that can be taken to support a coalitionbased approach to achieve this goal. For example, researchers and developers can establish technical mechanisms such as federated data management tools and explainable data value estimations, to inform and facilitate collective bargaining in the information economy. Additionally, regulatory and policy interventions may be introduced to support trusted data intermediary organizations representing guilds or syndicates of information producers…(More)”.
Upgrading Democracies with Fairer Voting Methods
Paper by Evangelos Pournaras, et al: “Voting methods are instrumental design element of democracies. Citizens use them to express and aggregate their preferences to reach a collective decision. However, voting outcomes can be as sensitive to voting rules as they are to people’s voting choices. Despite the significance and inter-disciplinary scientific progress on voting methods, several democracies keep relying on outdated voting methods that do not fit modern, pluralistic societies well, while lacking social innovation. Here, we demonstrate how one can upgrade real-world democracies, namely by using alternative preferential voting methods such as cumulative voting and the method of equal shares designed for a proportional representation of voters’ preferences. By rigorously assessing a new participatory budgeting approach applied in the city of Aarau, Switzerland, we unravel the striking voting outcomes of fair voting methods: more winning projects with the same budget and broader geographic and preference representation of citizens by the elected projects, in particular for voters who used to be under-represented, while promoting novel project ideas. We provide profound causal evidence showing that citizens prefer proportional voting methods, which possess strong legitimacy without the need of very technical specialized explanations. We also reveal strong underlying democratic values exhibited by citizens who support fair voting methods such as altruism and compromise. These findings come with a global momentum to unleash a new and long-awaited participation blueprint of how to upgrade democracies…(More)”.
Amplifying Human Creativity and Problem Solving with AI Through Generative Collective Intelligence
Paper by Thomas P. Kehler, Scott E. Page, Alex Pentland, Martin Reeves and John Seely Brown: “We propose a new framework for human-AI collaboration that amplifies the distinct capabilities
of both. This framework, which we call Generative Collective Intelligence (GCI), shifts AI to the
group/social level and employs AI in dual roles: as interactive agents and as technology that
accumulates, organizes, and leverages knowledge. By creating a cognitive bridge between
human reasoning and AI models, GCI can overcome limitations of purely algorithmic
approaches to problem-solving and decision-making. The framework demonstrates how AI can
be reframed as a social and cultural technology that enables groups to solve complex problems
through structured collaboration that transcends traditional communication barriers. We describe
the mathematical foundations of GCI based on comparative judgment and minimum regret
principles, and illustrate its applications across domains including climate adaptation, healthcare
transformation, and civic participation. By combining human creativity with AI’s computational
capabilities, GCI offers a promising approach to addressing complex societal challenges that
neither human or machines can solve alone…(More)”.
Leveraging Citizen Data to Improve Public Services and Measure Progress Toward Sustainable Development Goal 16
Paper by Dilek Fraisl: “This paper presents the results of a pilot study conducted in Ghana that utilized citizen data approaches for monitoring a governance indicator within the SDG framework, focusing on indicator 16.6.2 citizen satisfaction with public services. This indicator is a crucial measure of governance quality, as emphasized by the UN Sustainable Development Goals (SDGs) through target 16.6 Develop effective, accountable, and transparent institutions at all levels. Indicator 16.6.2 specifically measures satisfaction with key public services, including health, education, and other government services, such as government-issued identification documents through a survey. However, with only 5 years remaining to achieve the SDGs, the lack of data continues to pose a significant challenge in monitoring progress toward this target, particularly regarding the experiences of marginalized populations. Our findings suggest that well-designed citizen data initiatives can effectively capture the experiences of marginalized individuals and communities. Additionally, they can serve as valuable supplements to official statistics, providing crucial data on population groups typically underrepresented in traditional surveys…(More)”.