Searcharxiv⌕ Search

arXiv subjects

Sanja Šćepanović

Publications and source records attributed to Sanja Šćepanović.

At least 19 recordsLinked to original sources

Mobile Application Traffic Reveals Multifunctional Use Patterns in Parisian Parks

Urban parks play a key role in supporting public health. Landscape architecture typically considers parks through the lens of form and function. While past research on equitable access has focused mainly on park form, studies addressing functional uses have been constrained by limited scale and coarse measurement techniques. Existing efforts have partially quantified park functions through small-scale surveys and movement data or general usage data, but have not effectively captured the specific activities and motivations underlying park visits. As a result, our understanding of the functional roles urban parks play remains incomplete. We introduce a novel method that refines mobile base station coverage using antenna azimuths, enabling more precise distinction of mobile traffic within parks versus surrounding areas. Using Paris as a case study, we analyze a large-scale dataset of passively collected per-app mobile network traffic across 45 urban parks. We test two hypotheses: the Central-City hypothesis, which posits that multifunctional parks emerge in dense, high-rent areas due to land scarcity; and the Socio-Spatial hypothesis, which views parks as reflections of neighborhood routines and preferences. Our analysis shows that parks have distinctive mobile traffic signatures, differing from both their urban surroundings and from each other and identify three distinct functional types, Lunchbreak, Cultural, and Recreational parks, and analyze the traffic usage toward different motivations for visitation. Centrally located parks display more diverse app usage and pronounced temporal variation, while suburban parks reflect the digital behaviors of nearby communities, with app preferences aligned to neighborhood income. These findings demonstrate the value of mobile traffic as a proxy for studying the diversity of usage and activities within urban green spaces.

cs.CY↗

The Quiet Path from Seemingly Minor Design Errors to Workplace AI Incidents

Recent human-computer interaction (HCI) research has revealed a widespread misalignment between how developers design workplace artificial intelligence (AI) systems, and what workers actually need from them. Yet, little research has examined the effects of this gap, or how it may cause harm. We analyzed 1,524 reports of incidents in which AI systems were used to perform 171 occupational tasks across 12 industry sectors. Using an Large Language Model (LLM)-as-an-expert approach, we extracted the main traits of the AI systems involved in those incidents using an established framework of twelve traits. We then compared them with the traits that 202 workers highly familiar with those tasks would have preferred. We found that as many as 83\% of workplace incidents stem from worker-AI misalignments. In most cases, workers wanted systems that are precise, insightful, or personal, but instead received systems that are basic, simple, or general. Over the years, fast AI caused a considerable number of incidents, yet these declined, and imaginative AI, with the mass introduction of generative AI, started to cause incidents. We also compared the traits causing the incidents with the traits that 197 developers building AI systems for those tasks would have preferred. If the traits causing the incidents were the same as those designed by developers, then developers may be responsible for those incidents. We found that 74\% of task misalignments could be attributed to developers who tended to overfocus on efficiency and speed, especially for systems performing tasks in people-facing occupations such as those in the human resources sector. Our results call for design interventions that better align AI development with workers' needs, as without such corrections, workplace AI incidents are likely to persist, causing the invisible erosion of worker agency and organizational productivity.

cs.HC↗

When and How AI Should Assist Brainstorming for AI Impact Assessment

A key task in AI practice is to assess potential impacts to prevent harm. Current AI tools assisting AI impact assessment have not been designed or evaluated for collaborative team brainstorming, and they do not capture the range of views in diverse teams. We studied how AI can support team brainstorming during AI impact assessment and made three contributions. First, we adapted two structured methods from strategic foresight and co-designed AI interventions for them in five in-person workshops with 28 participants in total. Second, we evaluated the interventions in ten in-person workshops with 54 participants, finding that AI improved impact assessment quality and brainstorming perceptions for a general-purpose AI use (a chatbot companion) but not for a specialised one (a kidney allocation application). Third, our findings result in broader design guidance for AI assistance in brainstorming: AI should only offer hints and not solutions during early ideation, initiating interaction only when participants face fixation or saturation; it should facilitate structuring ideas during convergence; leverage expertise to refine ideas; and overall, it should serve more in support of tedious brainstorming process tasks, rather than ideation that teams value to do themselves.

cs.HC↗

Why AI Harms Can't Be Fixed One Identity at a Time: What 5300 Incident Reports Reveal About Intersectionality

AI risk assessment is the primary tool for identifying harms caused by AI systems. These include intersectional harms, which arise from the interaction between identity categories (e.g., class and skin tone) and which do not occur, or occur differently, when those categories are considered separately. Yet existing AI risk assessments are still built around isolated identity categories, and when intersections are considered, they focus almost exclusively on race and gender. Drawing on a large-scale analysis of documented AI incidents, we show that AI harms do not occur one identity category at a time. Using a structured rubric applied with a Large Language Model (LLM), we analyze 5,300 reports from 1,200 documented incidents in the AI Incident Database, the most curated source of incident data. From these reports, we identify 1,513 harmed subjects and their associated identity categories, achieving 98% accuracy. At the level of individual categories, we find that age and political identity appear in documented AI harms at rates comparable to race and gender. At the level of intersecting categories, harm is amplified up to three times at specific intersections: adolescent girls, lower-class people of color, and upper-class political elites. We argue that intersectionality should be a core component of AI risk assessment to more accurately capture how harms are produced and distributed across social groups.

cs.CY↗

Health-promoting Potential of Parks in 35 Cities Worldwide

Urban parks are important for public health, but the role of specific spaces, such as playgrounds or lakes, and elements, such as benches or sports equipment, in supporting well-being is not well understood. Based on expert input and a review of the literature, we defined six types of health-related activities: physical, mindfulness, nature appreciation, environmental, social, and cultural. We built a lexicon that links each activity to specific elements and spaces within parks present in OpenStreetMap. Using this data, we scored 23,477 parks across 35 cities worldwide based on their ability to support these activities. We found clear patterns: parks in North America focus more on physical activity, while those in Europe offer more chances to enjoy nature. Parks near city centers support health-promoting activities better than those farther out. Suburban parks in many cities lack the spaces and equipment needed for nature-based, social, and cultural activities. We also found large gaps in park quality between cities. Tokyo and Paris provide more equal access, while Copenhagen and Rio de Janeiro show sharp contrasts. These results can help cities create fairer parks that better support public health.

cs.CY↗

Impact Assessment Card: Communicating Risks and Benefits of AI Uses

Communicating the risks and benefits of AI is important for regulation and public understanding. Yet current methods such as technical reports often exclude people without technical expertise. Drawing on HCI research, we developed an Impact Assessment Card to present this information more clearly. We held three focus groups with a total of 12 participants who helped identify design requirements and create early versions of the card. We then tested a refined version in an online study with 235 participants, including AI developers, compliance experts, and members of the public selected to reflect the U.S. population by age, sex, and race. Participants used either the card or a full impact assessment report to write an email supporting or opposing a proposed AI system. The card led to faster task completion and higher-quality emails across all groups. We discuss how design choices can improve accessibility and support AI governance. Examples of cards are available at: https://social-dynamics.net/ai-risks/impact-card/.

cs.HC↗

The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms

We analyzed nearly 460,000 AI model cards from Hugging Face to examine how developers report risks. From these, we extracted around 3,000 unique risk mentions and built the \emph{AI Model Risk Catalog}. We compared these with risks identified by researchers in the MIT Risk Repository and with real-world incidents from the AI Incident Database. Developers focused on technical issues like bias and safety, while researchers emphasized broader social impacts. Both groups paid little attention to fraud and manipulation, which are common harms arising from how people interact with AI. Our findings show the need for clearer, structured risk reporting that helps developers think about human-interaction and systemic risks early in the design process. The catalog and paper appendix are available at: https://social-dynamics.net/ai-risks/catalog.

cs.CY↗

Vitamin N: Benefits of Different Forms of Public Greenery for Urban Health

Urban greenery is often linked to better health, yet findings from past research have been inconsistent. One reason is that official greenery metrics measure the amount or nearness of greenery but ignore how often people actually may potentially see or use it in daily life. To address this gap, we introduced a new classification that separates on-road greenery, which people see while walking through streets, from off-road greenery, which requires planned visits. We did so by combining aerial imagery of Greater London and greenery data from OpenStreetMap with quantified greenery from over 100,000 Google Street View images and accessibility estimates based on 160,000 road segments. We linked these measures to 7.45 billion medical prescriptions issued by the National Health Service and processed through our methodology. These prescriptions cover five conditions: diabetes, hypertension, asthma, depression, and anxiety, as well as opioid use. As hypothesized, we found that green on-road was more strongly linked to better health than four widely used official measures. For example, hypertension prescriptions dropped by 3.68% in wards with on-road greenery above the median citywide level compared to those below it. If all below-median wards reached the citywide median in on-road greenery, prescription costs could fall by up to £3.15 million each year. These results suggest that greenery seen in daily life may be more relevant than public yet secluded greenery, and that official metrics commonly used in the literature have important limitations.

cs.CY↗

The Potential Impact of Disruptive AI Innovations on U.S. Occupations

The rapid rise of AI is poised to disrupt the labor market. However, AI is not a monolith; its impact depends on both the nature of the innovation and the jobs it affects. While computational approaches are emerging, there is no consensus on how to systematically measure an innovation's disruptive potential. Here, we calculate the disruption index of 3,237 U.S. AI patents (2015-2022) and link them to job tasks to distinguish between "consolidating" AI innovations that reinforce existing structures and "disruptive" AI innovations that alter them. Our analysis reveals that consolidating AI primarily targets physical, routine, and solo tasks, common in manufacturing and construction in the Midwest and central states. By contrast, disruptive AI affects unpredictable and mental tasks, particularly in coastal science and technology sectors. Surprisingly, we also find that disruptive AI disproportionately affects areas already facing skilled labor shortages, suggesting disruptive AI technologies may accelerate change where workers are scarce rather than replacing a surplus. Ultimately, consolidating AI appears to extend current automation trends, while disruptive AI is set to transform complex mental work, with a notable exception for collaborative tasks.

cs.CY↗

RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reporting

Risk reporting is essential for documenting AI models, yet only 14% of model cards mention risks, out of which 96% copying content from a small set of cards, leading to a lack of actionable insights. Existing proposals for improving model cards do not resolve these issues. To address this, we introduce RiskRAG, a Retrieval Augmented Generation based risk reporting solution guided by five design requirements we identified from literature, and co-design with 16 developers: identifying diverse model-specific risks, clearly presenting and prioritizing them, contextualizing for real-world uses, and offering actionable mitigation strategies. Drawing from 450K model cards and 600 real-world incidents, RiskRAG pre-populates contextualized risk reports. A preliminary study with 50 developers showed that they preferred RiskRAG over standard model cards, as it better met all the design requirements. A final study with 38 developers, 40 designers, and 37 media professionals showed that RiskRAG improved their way of selecting the AI model for a specific application, encouraging a more careful and deliberative decision-making. The RiskRAG project page is accessible at: https://social-dynamics.net/ai-risks/card.

cs.SE↗

Atlas of AI Risks: Enhancing Public Understanding of AI Risks

The prevailing methodologies for visualizing AI risks have focused on technical issues such as data biases and model inaccuracies, often overlooking broader societal risks like job loss and surveillance. Moreover, these visualizations are typically designed for tech-savvy individuals, neglecting those with limited technical skills. To address these challenges, we propose the Atlas of AI Risks-a narrative-style tool designed to map the broad risks associated with various AI technologies in a way that is understandable to non-technical individuals as well. To both develop and evaluate this tool, we conducted two crowdsourcing studies. The first, involving 40 participants, identified the design requirements for visualizing AI risks for decision-making and guided the development of the Atlas. The second study, with 140 participants reflecting the US population in terms of age, sex, and ethnicity, assessed the usability and aesthetics of the Atlas to ensure it met those requirements. Using facial recognition technology as a case study, we found that the Atlas is more user-friendly than a baseline visualization, with a more classic and expressive aesthetic, and is more effective in presenting a balanced assessment of the risks and benefits of facial recognition. Finally, we discuss how our design choices make the Atlas adaptable for broader use, allowing it to generalize across the diverse range of technology applications represented in a database that reports various AI incidents.

cs.HC↗

How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data

Health outcomes depend on complex environmental and sociodemographic factors whose effects change over location and time. Only recently has fine-grained spatial and temporal data become available to study these effects, namely the MEDSAT dataset of English health, environmental, and sociodemographic information. Leveraging this new resource, we use a variety of variable importance techniques to robustly identify the most informative predictors across multiple health outcomes. We then develop an interpretable machine learning framework based on Generalized Additive Models (GAMs) and Multiscale Geographically Weighted Regression (MGWR) to analyze both local and global spatial dependencies of each variable on various health outcomes. Our findings identify NO2 as a global predictor for asthma, hypertension, and anxiety, alongside other outcome-specific predictors related to occupation, marriage, and vegetation. Regional analyses reveal local variations with air pollution and solar radiation, with notable shifts during COVID. This comprehensive approach provides actionable insights for addressing health disparities, and advocates for the integration of interpretable machine learning in public health.

cs.LG↗

Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts

In the evolving landscape of AI regulation, it is crucial for companies to conduct impact assessments and document their compliance through comprehensive reports. However, current reports lack grounding in regulations and often focus on specific aspects like privacy in relation to AI systems, without addressing the real-world uses of these systems. Moreover, there is no systematic effort to design and evaluate these reports with both AI practitioners and AI compliance experts. To address this gap, we conducted an iterative co-design process with 14 AI practitioners and 6 AI compliance experts and proposed a template for impact assessment reports grounded in the EU AI Act, NIST's AI Risk Management Framework, and ISO 42001 AI Management System. We evaluated the template by producing an impact assessment report for an AI-based meeting companion at a major tech company. A user study with 8 AI practitioners from the same company and 5 AI compliance experts from industry and academia revealed that our template effectively provides necessary information for impact assessments and documents the broad impacts of AI systems. Participants envisioned using the template not only at the pre-deployment stage for compliance but also as a tool to guide the design stage of AI uses.

cs.HC↗

The Atlas of AI Incidents in Mobile Computing: Visualizing the Risks and Benefits of AI Gone Mobile

Today's visualization tools for conveying the risks and benefits of AI technologies are largely tailored for those with technical expertise. To bridge this gap, we have developed a visualization that employs narrative patterns and interactive elements, enabling the broader public to gradually grasp the diverse risks and benefits associated with AI. Using a dataset of 54 real-world incidents involving AI in mobile computing, we examined design choices that enhance public understanding and provoke reflection on how certain AI applications - even those deemed low-risk by law - can still lead to significant incidents. Visualization: https://social-dynamics.net/mobile-ai-risks

cs.HC↗

ExploreGen: Large Language Models for Envisioning the Uses and Risks of AI Technologies

Responsible AI design is increasingly seen as an imperative by both AI developers and AI compliance experts. One of the key tasks is envisioning AI technology uses and risks. Recent studies on the model and data cards reveal that AI practitioners struggle with this task due to its inherently challenging nature. Here, we demonstrate that leveraging a Large Language Model (LLM) can support AI practitioners in this task by enabling reflexivity, brainstorming, and deliberation, especially in the early design stages of the AI development process. We developed an LLM framework, ExploreGen, which generates realistic and varied uses of AI technology, including those overlooked by research, and classifies their risk level based on the EU AI Act regulation. We evaluated our framework using the case of Facial Recognition and Analysis technology in nine user studies with 25 AI practitioners. Our findings show that ExploreGen is helpful to both developers and compliance experts. They rated the uses as realistic and their risk classification as accurate (94.5%). Moreover, while unfamiliar with many of the uses, they rated them as having high adoption potential and transformational impact.

cs.HC↗

Dream Content Discovery from Reddit with an Unsupervised Mixed-Method Approach

Dreaming is a fundamental but not fully understood part of human experience that can shed light on our thought patterns. Traditional dream analysis practices, while popular and aided by over 130 unique scales and rating systems, have limitations. Mostly based on retrospective surveys or lab studies, they struggle to be applied on a large scale or to show the importance and connections between different dream themes. To overcome these issues, we developed a new, data-driven mixed-method approach for identifying topics in free-form dream reports through natural language processing. We tested this method on 44,213 dream reports from Reddit's r/Dreams subreddit, where we found 217 topics, grouped into 22 larger themes: the most extensive collection of dream topics to date. We validated our topics by comparing it to the widely-used Hall and van de Castle scale. Going beyond traditional scales, our method can find unique patterns in different dream types (like nightmares or recurring dreams), understand topic importance and connections, and observe changes in collective dream experiences over time and around major events, like the COVID-19 pandemic and the recent Russo-Ukrainian war. We envision that the applications of our method will provide valuable insights into the intricate nature of dreaming.

cs.CY↗

Photometry of Type II Supernova SN 2023ixf with a Worldwide Citizen Science Network

We present highly sampled photometry of the supernova (SN) 2023ixf, a Type II SN in M101, beginning 2 days before its first known detection. To gather these data, we enlisted the global Unistellar Network of citizen scientists. These 252 observations from 115 telescopes show the SN's rising brightness associated with shock emergence followed by gradual decay. We measure a peak $M_{V}$ = -18.18 $\pm$ 0.09 mag at 2023-05-25 21:37 UTC in agreement with previously published analyses.

astro-ph.HE↗

Insider Stories: Analyzing Internal Sustainability Efforts of Major US Companies from Online Reviews

It is hard to establish whether a company supports internal sustainability efforts (ISEs) like gender equality, diversity, and general staff welfare, not least because of lack of methodologies operationalizing these internal sustainability practices, and of data honestly documenting such efforts. We developed and validated a six-dimension framework reflecting Internal Sustainability Efforts (ISEs), gathered more than 350K employee reviews of 104 major companies across the whole US for the (2008-2020) years, and developed a deep-learning framework scoring these reviews in terms of the six ISEs. Commitment to ISEs manifested itself at micro-level -- companies scoring high in ISEs enjoyed high stock growth. This new conceptualization of ISEs offers both theoretical implications for the literature in corporate sustainability, and practical implications for companies and policymakers. To further explore these implications, researchers need to add potentially missing ISEs, to do so for more companies, and establish the causal relationship between company success and ISEs.

cs.SI↗