Paper by Francesca Di Giuseppe, Joe McNorton, Anna Lombardi & Fredrik Wetterhall: “Recent advancements in machine learning (ML) have expanded the potential use across scientific applications, including weather and hazard forecasting. The ability of these methods to extract information from diverse and novel data types enables the transition from forecasting fire weather, to predicting actual fire activity. In this study we demonstrate that this shift is feasible also within an operational context. Traditional methods of fire forecasts tend to over predict high fire danger, particularly in fuel limited biomes, often resulting in false alarms. By using data on fuel characteristics, ignitions and observed fire activity, data-driven predictions reduce the false-alarm rate of high-danger forecasts, enhancing their accuracy. This is made possible by high quality global datasets of fuel evolution and fire detection. We find that the quality of input data is more important when improving forecasts than the complexity of the ML architecture. While the focus on ML advancements is often justified, our findings highlight the importance of investing in high-quality data and, where necessary create it through physical models. Neglecting this aspect would undermine the potential gains from ML-based approaches, emphasizing that data quality is essential to achieve meaningful progress in fire activity forecasting…(More)”.
Exploring Human Mobility in Urban Nightlife: Insights from Foursquare Data
Article by Ehsan Dorostkar: “In today’s digital age, social media platforms like Foursquare provide a wealth of data that can reveal fascinating insights into human behavior, especially in urban environments. Our recent study, published in Cities, delves into how virtual mobility on Foursquare translates into actual human mobility in Tehran’s nightlife scenes. By analyzing user-generated data, we uncovered patterns that can help urban planners create more vibrant and functional nightlife spaces…
Our study aimed to answer two key questions:
- How does virtual mobility on Foursquare influence real-world human mobility in urban nightlife?
- What spatial patterns emerge from these movements, and how can they inform urban planning?
To explore these questions, we focused on two bustling nightlife spots in Tehran—Region 1 (Darband Square) and Region 6 (Valiasr crossroads)—where Foursquare data indicated high user activity.
Methodology
We combined data from two sources:
- Foursquare API: To track user check-ins and identify popular nightlife venues.
- Tehran Municipality API: To contextualize the data within the city’s urban framework.
Using triangulation and interpolation techniques, we mapped the “human mobility triangles” in these areas, calculating the density and spread of user activity…(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)”.
LLM Social Simulations Are a Promising Research Method
Paper by Jacy Reese Anthis et al: “Accurate and verifiable large language model (LLM) simulations of human research subjects promise an accessible data source for understanding human behavior and training new AI systems. However, results to date have been limited, and few social scientists have adopted these methods. In this position paper, we argue that the promise of LLM social simulations can be achieved by addressing five tractable challenges. We ground our argument in a literature survey of empirical comparisons between LLMs and human research subjects, commentaries on the topic, and related work. We identify promising directions with prompting, fine-tuning, and complementary methods. We believe that LLM social simulations can already be used for exploratory research, such as pilot experiments for psychology, economics, sociology, and marketing. More widespread use may soon be possible with rapidly advancing LLM capabilities, and researchers should prioritize developing conceptual models and evaluations that can be iteratively deployed and refined at pace with ongoing AI advances…(More)”.
Situating Digital Self-Determination (DSD): A Comparison with Existing and Emerging Digital and Data Governance Approaches
Paper by Sara Marcucci and Stefaan Verhulst: “In today’s increasingly complex digital landscape, traditional data governance models-such as consent-based, ownership-based, and sovereignty-based approaches-are proving insufficient to address the evolving ethical, social, and political dimensions of data use. These frameworks, often grounded in static and individualistic notions of control, struggle to keep pace with the fluidity and relational nature of contemporary data ecosystems. This paper proposes Digital Self-Determination (DSD) as a complementary and necessary evolution of existing models, offering a more participatory, adaptive, and ethically grounded approach to data governance. Centering ongoing agency, collective participation, and contextual responsiveness, DSD builds on foundational principles of consent and control while addressing their limitations. Drawing on comparisons with a range of governance models-including risk-based, compliance-oriented, principles-driven, and justice-centered frameworks-this paper highlights DSD’s unique contribution: its capacity to enable individuals and communities to actively shape how data about them is used, shared, and governed over time. In doing so, it reimagines data governance as a living, co-constructed practice grounded in trust, accountability, and care. Through this lens, the paper offers a framework for comparing different governance approaches and embedding DSD into existing paradigms, inviting policymakers and practitioners to consider how more inclusive and responsive forms of digital governance might be realized…(More)”.
Digital Technologies and Participatory Governance in Local Settings: Comparing Digital Civic Engagement Initiatives During the COVID-19 Outbreak
Chapter by Nathalie Colasanti, Chiara Fantauzzi, Rocco Frondizi & Noemi Rossi: “Governance paradigms have undergone a deep transformation during the COVID-19 pandemic, necessitating agile, inclusive, and responsive mechanisms to address evolving challenges. Participatory governance has emerged as a guiding principle, emphasizing inclusive decision-making processes and collaboration among diverse stakeholders. In the outbreak context, digital technologies have played a crucial role in enabling participatory governance to flourish, democratizing participation, and facilitating the rapid dissemination of accurate information. These technologies have also empowered grassroots initiatives, such as civic hacking, to address societal challenges and mobilize communities for collective action. This study delves into the realm of bottom-up participatory initiatives at the local level, focusing on two emblematic cases of civic hacking experiences launched during the pandemic, the first in Wuhan, China, and the second in Italy. Through a comparative lens, drawing upon secondary sources, the aim is to analyze the dynamics, efficacy, and implications of these initiatives, shedding light on the evolving landscape of participatory governance in times of crisis. Findings underline the transformative potential of civic hacking and participatory governance in crisis response, highlighting the importance of collaboration, transparency, and inclusivity…(More)”.
Paths to Social Licence for Tracking-data Analytics
Paper by Joshua P. White: “While tracking-data analytics can be a goldmine for institutions and companies, the inherent privacy concerns also form a legal, ethical and social minefield. We present a study that seeks to understand the extent and circumstances under which tracking-data analytics is undertaken with social licence — that is, with broad community acceptance beyond formal compliance with legal requirements. Taking a University campus environment as a case, we enquire about the social licence for Wi-Fi-based tracking-data analytics. Staff and student participants answered a questionnaire presenting hypothetical scenarios involving Wi-Fi tracking for university research and services. Our results present a Bayesian logistic mixed-effects regression of acceptability judgements as a function of participant ratings on 11 privacy dimensions. Results show widespread acceptance of tracking-data analytics on campus and suggest that trust, individual benefit, data sensitivity, risk of harm and institutional respect for privacy are the most predictive factors determining this acceptance judgement…(More)”.
Prosocial Media
Paper by Glen Weyl et al: “Social media empower distributed content creation by algorithmically harnessing “the social fabric” (explicit and implicit signals of association) to serve this content. While this overcomes the bottlenecks and biases of traditional gatekeepers, many believe it has unsustainably eroded the very social fabric it depends on by maximizing engagement for advertising revenue. This paper participates in open and ongoing considerations to translate social and political values and conventions, specifically social cohesion, into platform design. We propose an alternative platform model that includes the social fabric an explicit output as well as input. Citizens are members of communities defined by explicit affiliation or clusters of shared attitudes. Both have internal divisions, as citizens are members of intersecting communities, which are themselves internally diverse. Each is understood to value content that bridge (viz. achieve consensus across) and balance (viz. represent fairly) this internal diversity, consistent with the principles of the Hutchins Commission (1947). Content is labeled with social provenance, indicating for which community or citizen it is bridging or balancing. Subscription payments allow citizens and communities to increase the algorithmic weight on the content they value in the content serving algorithm. Advertisers may, with consent of citizen or community counterparties, target them in exchange for payment or increase in that party’s algorithmic weight. Underserved and emerging communities and citizens are optimally subsidized/supported to develop into paying participants. Content creators and communities that curate content are rewarded for their contributions with algorithmic weight and/or revenue. We discuss applications to productivity (e.g. LinkedIn), political (e.g. X), and cultural (e.g. TikTok) platforms…(More)”.
Researching data discomfort: The case of Statistics Norway’s quest for billing data
Paper by Lisa Reutter: “National statistics offices are increasingly exploring the possibilities of utilizing new data sources to position themselves in emerging data markets. In 2022, Statistics Norway announced that the national agency will require the biggest grocers in Norway to hand over all collected billing data to produce consumer behavior statistics which had previously been produced by other sampling methods. An online article discussing this proposal sparked a surprisingly (at least to Statistics Norway) high level of interest among readers, many of whom expressed concerns about this intended change in data practice. This paper focuses on the multifaceted online discussions of the proposal, as these enable us to study citizens’ reactions and feelings towards increased data collection and emerging public-private data flows in a Nordic context. Through an explorative empirical analysis of comment sections, this paper investigates what is discussed by commenters and reflects upon why this case sparked so much interest among citizens in the first place. It therefore contributes to the growing literature of citizens’ voices in data-driven administration and to a wider discussion on how to research public feeling towards datafication. I argue that this presents an interesting case of discomfort voiced by citizens, which demonstrates the contested nature of data practices among citizens–and their ability to regard data as deeply intertwined with power and politics. This case also reminds researchers to pay attention to seemingly benign and small changes in administration beyond artificial intelligence…(More)”
Towards a set of Universal data principles
Paper by Steve MacFeely, Angela Me, Friederike Schueuer, Joseph Costanzo, David Passarelli, Malarvizhi Veerappan, and Stefaan Verhulst: “Humanity collects, processes, shares, uses, and reuses a staggering volume of data. These data are the lifeblood of the digital economy; they feed algorithms and artificial intelligence, inform logistics, and shape markets, communication, and politics. Data do not just yield economic benefits; they can also have individual and societal benefits and impacts. Being able to access, process, use, and reuse data is essential for dealing with global challenges, such as managing and protecting the environment, intervening in the event of a pandemic, or responding to a disaster or crisis. While we have made great strides, we have yet to realize the full potential of data, in particular, the potential of data to serve the public good. This will require international cooperation and a globally coordinated approach. Many data governance issues cannot be fully resolved at national level. This paper presents a proposal for a preliminary set of data goals and principles. These goals and principles are envisaged as the normative foundations for an international data governance framework – one that is grounded in human rights and sustainable development. A principles-based approach to data governance helps create common values, and in doing so, helps to change behaviours, mindsets and practices. It can also help create a foundation for the safe use of all types of data and data transactions. The purpose of this paper is to present the preliminary principles to solicit reaction and feedback…(More)”.