Practitioner perspectives on informing decisions in One Health sectors with predictive models

Curated on Posted on by Stefaan Verhulst

Paper by Kim M. Pepin: “Every decision a person makes is based on a model. A model is an idea about how a process works based on previous experience, observation, or other data. Models may not be explicit or stated (Johnson-Laird, 2010), but they serve to simplify a complex world. Models vary dramatically from conceptual (idea) to statistical (mathematical expression relating observed data to an assumed process and/or other data) or analytical/computational (quantitative algorithm describing a process). Predictive models of complex systems describe an understanding of how systems work, often in mathematical or statistical terms, using data, knowledge, and/or expert opinion. They provide means for predicting outcomes of interest, studying different management decision impacts, and quantifying decision risk and uncertainty (Berger et al. 2021; Li et al. 2017). They can help decision-makers assimilate how multiple pieces of information determine an outcome of interest about a complex system (Berger et al. 2021; Hemming et al. 2022).

People rely daily on system-level models to reach objectives. Choosing the fastest route to a destination is one example. Such a decision may be based on either a mental model of the road system developed from previous experience or a traffic prediction mapping application based on mathematical algorithms and current data. Either way, a system-level model has been applied and there is some uncertainty. In contrast, predicting outcomes for new and complex phenomena, such as emerging disease spread, a biological invasion risk (Chen et al. 2023; Elderd et al. 2006; Pepin et al. 2022), or climatic impacts on ecosystems is more uncertain. Here public service decision-makers may turn to mathematical models when expert opinion and experience do not resolve enough uncertainty about decision outcomes. But using models to guide decisions also relies on expert opinion and experience. Also, even technical experts need to make modeling choices regarding model structure and data inputs that have uncertainty (Elderd et al. 2006) and these might not be completely objective decisions (Bedson et al. 2021). Thus, using models for guiding decisions has subjectivity from both the developer and end-user, which can lead to apprehension or lack of trust about using models to inform decisions.

Models may be particularly advantageous to decision-making in One Health sectors, including health of humans, agriculture, wildlife, and the environment (hereafter called One Health sectors) and their interconnectedness (Adisasmito et al. 2022)…(More)”.

AI-enhanced nudging in public policy: why to worry and how to respond

Curated on Posted on by Stefaan Verhulst

Paper by Stefano Calboli & Bart Engelen: “What role can artificial intelligence (AI) play in enhancing public policy nudges and the extent to which these help people achieve their own goals? Can it help mitigate or even overcome the challenges that nudgers face in this respect? This paper discusses how AI-enhanced personalization can help make nudges more means paternalistic and thus more respectful of people’s ends. We explore the potential added value of AI by analyzing to what extent it can, (1) help identify individual preferences and (2) tailor different nudging techniques to different people based on variations in their susceptibility to those techniques. However, we also argue that the successes booked in this respect in the for-profit sector cannot simply be replicated in public policy. While AI can bring benefits to means paternalist public policy nudging, it also has predictable downsides (lower effectiveness compared to the private sector) and risks (graver consequences compared to the private sector). We discuss the practical implications of all this and propose novel strategies that both consumers and regulators can employ to respond to private AI use in nudging with the aim of safeguarding people’s autonomy and agency…(More)”. See also: Engagement Integrity: Ensuring Legitimacy at a time of AI-Augmented Participation

Misinformation by Omission: The Need for More Environmental Transparency in AI

Curated on Posted on by Stefaan Verhulst

Paper by Sasha Luccioni, Boris Gamazaychikov, Theo Alves da Costa, and Emma Strubell: “In recent years, Artificial Intelligence (AI) models have grown in size and complexity, driving greater demand for computational power and natural resources. In parallel to this trend, transparency around the costs and impacts of these models has decreased, meaning that the users of these technologies have little to no information about their resource demands and subsequent impacts on the environment. Despite this dearth of adequate data, escalating demand for figures quantifying AI’s environmental impacts has led to numerous instances of misinformation evolving from inaccurate or de-contextualized best-effort estimates of greenhouse gas emissions. In this article, we explore pervasive myths and misconceptions shaping public understanding of AI’s environmental impacts, tracing their origins and their spread in both the media and scientific publications. We discuss the importance of data transparency in clarifying misconceptions and mitigating these harms, and conclude with a set of recommendations for how AI developers and policymakers can leverage this information to mitigate negative impacts in the future…(More)”.

How to Make Small Beautiful: The Promise of Democratic Innovations

Curated on Posted on by Stefaan Verhulst

Paper by Christoph Niessen & Wouter Veenendaal: “Small states are on average more likely to be democracies and it is often assumed that democracy functions better in small polities. ‘Small is beautiful’, proponents say. Yet, empirical scholarship shows that, while smallness comes with socio-political proximity, which facilitates participation and policy implementation, it also incentivizes personalism, clientelism and power concentration. Largeness, instead, comes with greater socio-political distance, but strengthens institutional checks and entails scale advantages. In this article, we depart from this trade-off and, wondering ‘how to make small beautiful’, we examine a potential remedy: democratic innovations. To do so, we first show that representative institutions were adopted in small polities by replication rather than by choice, and that they can aggravate the democratic problems associated with smallness. Subsequently, we draw on four usages of direct and deliberative democratic practices in small polities to explore which promises they offer to correct some of these pitfalls…(More)”.

Comparative evaluation of behavioral epidemic models using COVID-19 data

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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.