Paper by Marc Schuilenburg and Yarin Eski: “Why do people voluntarily give away their personal data to private companies? In this paper, we show how data sharing is experienced at the level of Tesla car owners. We regard Tesla cars as luxury surveillance goods for which the drivers voluntarily choose to share their personal data with the US company. Based on an analysis of semi-structured interviews and observations of Tesla owners’ posts on Facebook groups, we discern three elements of luxury surveillance: socializing, enjoying and enduring. We conclude that luxury surveillance can be traced back to the social bonds created by a gift economy…(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)”.
Global data-driven prediction of fire activity
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)”.