Adults with intellectual disabilities (ID) face systemic social exclusion that narrows autonomy and life opportunities. While social virtual reality (VR) offers a powerful medium for identity expression and community belonging, research often adopts a remedial paradigm, focusing on training functional skills in scripted environments. This paper challenges this deficit-based model by treating social VR as an open world for participation. Following 11 adults with ID across multi-session engagements with VRChat, we employed an adaptive, relational method to scaffold participant leadership. Findings reveal that participants used the platform for interest-driven discovery, sustained through interdependent care webs. Crucially, the study demonstrates how social VR supports transferable confidence and emerging digital citizenship, enabling some users to transition from novices to community leaders. We contribute six Disability Justice-aligned design principles articulating a world-making paradigm that reorients Human-Computer Interaction toward supporting personhood and self-determination in mainstream digital publics.

Personal tracking technologies for family life, including speech tracking, have long been conceptualised in HCI as tools to support wellbeing and parenting at scale. However, speech tracking is inherently invasive because it captures intimate family interactions, and its ethical introduction and appropriation into everyday life remains uncertain. Using research through design, we examined family speech tracking by developing seven plausible design fictions grounded in parenting theory, and then conducting a 1-week technology probe study with 60 parents to understand their perceptions. Parents envisaged both positive and negative outcomes for family speech tracking, including concerns about diminishing authentic connection and potential misappropriation. Unlike existing parenting interventions, parents envisioned technologies supporting children’s intervention goals directly rather than focusing on parental skill development. We contribute seven evidence-informed speculative speech tracking concepts, empirical insights revealing concerns about diminishing authentic parent-child connection, and identification of technical, interactional, and systemic sociotechnical dilemmas.

The mindset people have about stress is important to be studied because this core belief, that stress is either enhancing or debilitating, fundamentally alters a person’s physiological and psychological responses to stressors. However, this crucial construct is rarely considered in prior research on momentary stress detection with wearables, leaving two fundamental questions unanswered: can wearable data identify an individual’s stress mindset, and can mindset be leveraged to build better performing stress detection models? To investigate that, we conducted an in-lab study with wearable devices by inducing mental stress in participants (N=23). First, we found that heart rate variability and electrodermal activity features carry signatures of stress mindset. Second, machine learning models can discriminate stress mindset with sensors, achieving AUCs upto 0.88. Finally, a random forest model trained for stress-is-enhancing participants outperformed a one-size-fits-all model (AUC=0.91 vs. 0.78, p < 0.05), for the task of stress detection. Our findings show that stress mindset leaves a measurable physiological footprint and that mindset-aware models open the potential for more personalized stress detection and interventions. To support future research, we publicly release the anonymized dataset at https://social-dynamics.net/stress/mindset

Music is increasingly performed and experienced in Social Virtual Reality (Social VR), from VRChat raves to high-production concerts on bespoke platforms. Yet Human-Computer-Interaction (HCI) research still focuses mainly on building new VR systems rather than examining the communities that already create and sustain these practices. We present a cultural mapping of the Social VR music scene based on 84 survey responses, 27 interviews, and 17 event observations with diverse stakeholders, including audience members, musicians, developers, platform owners, and event organisers. We found that the scene operates as a fragmented cross-platform ecosystem sustained by user-generated infrastructure and continuous community labour. The bottom-up organisation produces role fluidity with individuals dynamically shifting between roles as performers, world builders, organisers, and audience members. However, the openness that enables this creativity also creates tensions between expectations of free access and the financial and emotional labour required to keep events running. Taken together, our findings reveal the vibrant cultural practices that continue to flourish in Social VR, even as corporate narratives declare the “metaverse” dead.

Virtual Reality (VR) is increasingly being used to support workplace well-being, but many interventions focus narrowly on a single activity or goal. Our work explores how VR can meet the diverse physical and mental needs of knowledge workers. We developed Tranquil Loom, a VR app offering stretching, guided meditation, and open exploration across four environments. The app includes an AI assistant that suggests activities based on users emotional states. We conducted a two-phase mixed-methods study: (1) interviews with 10 knowledge workers to guide the apps design, and (2) deployment with 35 participants gathering usage data, well-being measures, and interviews. Results showed increases in mindfulness and reductions in anxiety. Participants enjoyed both structured and open-ended activities, often using the app playfully. While AI suggestions were used infrequently, they prompted ideas for future personalization. Overall, participants viewed VR as a flexible, “dropin” tool, highlighting its value for situational rather than prescriptive well-being support.

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/

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.

Translational research, especially in the fast-evolving field of Artificial Intelligence (AI), is key to converting scientific findings into practical innovations. In Responsible AI (RAI) research, translational impact is often viewed through various pathways, including research papers, blogs, news articles, and the drafting of forthcoming AI legislation (e.g., the EU AI Act). However, the real-world impact of RAI research remains an underexplored area. Our study aims to capture it through two pathways: patents and code repositories, both of which provide a rich and structured source of data. Using a dataset of 200,000 papers from 1980 to 2022 in AI and related fields, including Computer Vision, Natural Language Processing, and Human-Computer Interaction, we developed a Sentence-Transformers Deep Learning framework to identify RAI papers. This framework calculates the semantic similarity between paper abstracts and a set of RAI keywords, which are derived from the NIST's AI Risk Management Framework; a framework that aims to enhance trustworthiness considerations in the design, development, use, and evaluation of AI products, services, and systems. We identified 1,747 RAI papers published in top venues such as CHI, CSCW, NeurIPS, FAccT, and AIES between 2015 and 2022. By analyzing these papers, we found that a small subset that goes into patents or repositories is highly cited, with the translational process taking between 1 year for repositories and up to 8 years for patents. Interestingly, impactful RAI research is not limited to top U.S. institutions, but significant contributions come from European and Asian institutions. Finally, the multidisciplinary nature of RAI papers, often incorporating knowledge from diverse fields of expertise, was evident as these papers tend to build on unconventional combinations of prior knowledge.

As Artificial Intelligence (AI) becomes ubiquitous, the need for Explainable AI (XAI) has become critical for transparency and trust among users. A significant challenge in XAI is catering to diverse users, such as data scientists, domain experts, and end-users. Recent research has started to investigate how users’ characteristics impact interactions with and user experience of explanations, with a view to personalizing XAI. However, are we heading down a rabbit hole by focusing on unimportant details? Our research aimed to investigate how user characteristics are related to using, understanding, and trusting an AI system that provides explanations. Our empirical study with 149 participants who interacted with an XAI system that flagged inappropriate comments showed that very few user characteristics mattered; only age and the personality trait openness influenced actual understanding. Our work provides evidence to reorient user-focused XAI research and question the pursuit of personalized XAI based on fine-grained user characteristics.

Value Sensitive Design (VSD) is a framework for integrating human values throughout the technology design process. In parallel, Responsible AI (RAI) advocates for the development of systems aligning with ethical values, such as fairness and transparency. In this study, we posit that a VSD approach is not only compatible, but also advantageous to the development of RAI toolkits. To empirically assess this hypothesis, we conducted four workshops involving 17 early-career AI researchers. Our aim was to establish links between VSD and RAI values while examining how existing toolkits incorporate VSD principles in their design. Our findings show that collaborative and educational design features within these toolkits, including illustrative examples and open-ended cues, facilitate an understanding of human and ethical values, and empower researchers to incorporate values into AI systems. Drawing on these insights, we formulated six design guidelines for integrating VSD values into the development of RAI toolkits.

Self-supervised learning (SSL) has become the de facto training paradigm of large models, where pre-training is followed by supervised fine-tuning using domain-specific data and labels. Despite demonstrating comparable performance with supervised methods, comprehensive efforts to assess SSL's impact on machine learning fairness (i.e., performing equally on different demographic breakdowns) are lacking. Hypothesizing that SSL models would learn more generic, hence less biased representations, this study explores the impact of pre-training and fine-tuning strategies on fairness. We introduce a fairness assessment framework for SSL, comprising five stages: defining dataset requirements, pre-training, fine-tuning with gradual unfreezing, assessing representation similarity conditioned on demographics, and establishing domain-specific evaluation processes. We evaluate our method's generalizability on three real-world human-centric datasets (i.e., MIMIC, MESA, and GLOBEM) by systematically comparing hundreds of SSL and fine-tuned models on various dimensions spanning from the intermediate representations to appropriate evaluation metrics. Our findings demonstrate that SSL can significantly improve model fairness, while maintaining performance on par with supervised methods-exhibiting up to a 30% increase in fairness with minimal loss in performance through self-supervision. We posit that such differences can be attributed to representation dissimilarities found between the best- and the worst-performing demographics across models-up to x13 greater for protected attributes with larger performance discrepancies between segments. Code: https://github.com/Nokia-Bell-Labs/SSLfairness

Integrating Artificial Intelligence (AI) into mobile and wearables offers numerous benefits at individual, societal, and environmental levels. Yet, it also spotlights concerns over emerging risks. Traditional assessments of risks and benefits have been sporadic, and often require costly expert analysis. We developed a semi-automatic method that leverages Large Language Models (LLMs) to identify AI uses in mobile and wearables, classify their risks based on the EU AI Act, and determine their benefits that align with globally recognized long-term sustainable development goals; a manual validation of our method by two experts in mobile and wearable technologies, a legal and compliance expert, and a cohort of nine individuals with legal backgrounds who were recruited from Prolific, confirmed its accuracy to be over 85%. We uncovered that specific applications of mobile computing hold significant potential in improving well-being, safety, and social equality. However, these promising uses are linked to risks involving sensitive data, vulnerable groups, and automated decision-making. To avoid rejecting these risky yet impactful mobile and wearable uses, we propose a risk assessment checklist for the Mobile HCI community.

Many guidelines for responsible AI have been suggested to help AI practitioners in the development of ethical and responsible AI systems. However, these guidelines are often neither grounded in regulation nor usable by different roles, from developers to decision makers. To bridge this gap, we developed a four-step method to generate a list of responsible AI guidelines; these steps are: (1) manual coding of 17 papers on responsible AI; (2) compiling an initial catalog of responsible AI guidelines; (3) refining the catalog through interviews and expert panels; and (4) finalizing the catalog. To evaluate the resulting 22 guidelines, we incorporated them into an interactive tool and assessed them in a user study with 14 AI researchers, engineers, designers, and managers from a large technology company. Through interviews with these practitioners, we found that the guidelines were grounded in current regulations and usable across roles, encouraging self-reflection on ethical considerations at early stages of development. This significantly contributes to the concept of 'Responsible AI by Design'- a design-first approach that embeds responsible AI values throughout the development lifecycle and across various business roles.

Being able to assess dog personality can be used to, for example, match shelter dogs with future owners, and personalize dog activities. Such an assessment typically relies on experts or psychological scales administered to dog owners, both of which are costly. To tackle that challenge, we built a device called “Patchkeeper” that can be strapped on the pet’s chest and measures activity through an accelerometer and a gyroscope. In an in-the-wild deployment involving 12 healthy dogs, we collected 1300 hours of sensor activity data and dog personality test results from two validated questionnaires. By matching these two datasets, we trained ten machine learning classifiers that predicted dog personality from activity data, achieving AUCs in [0.63-0.90], suggesting the value of tracking psychological signals of pets using wearable technologies.

Circadian rhythm has been linked to both physical and mental health at an individual level in prior research. Such a link at population level has been long hypothesized but has never been tested, largely because of lack of data. To partly fix this literature gap, we need: a dataset on population-level circadian rhythms, a dataset on population-level health conditions, and strong associations between these two partly independent sets. Recent work has shown that affect on social media data relates to population-level circadian rhythms. Building upon that work, we extracted five circadian rhythm metrics from 6M Reddit posts across 18 major cities (for which the number of residents is highly correlated with the number of users), and paired them with three ground-truth health metrics (daily number of steps, sleep quantity, and sleep quality) extracted from 233K wearable users in these cities. We found that rhythms of online activity approximated sleeping patterns rather than, what the literature previously hypothesized, alertness levels. Despite that, we found that these rhythms, when computed in two specific times of the day (i.e., late at night and early morning), were still predictive of the three ground-truth health metrics: in general, healthier cities had morning spikes on social media, night dips, and expressions of positive affect. These results suggest that circadian rhythms on social media, if taken at two specific times of the day and operationalized with literature-driven metrics, can approximate the temporal evolution of people's shared underlying biological rhythm as it relates to physical activity (R2=0.492), sleep quantity (R2=0.765), and sleep quality (R2=0.624).

Studies on depression in the workplace have mostly investigated its impact on individual employees. Little is known about its association with the company as a whole, or the state where the company is based. This is due to the lack of scalable methodologies operationalizing depression in the specific context of the workplace, and of data documenting potential distress. In this work, we adapted a work-related depression scale called Occupational Depression Inventory (ODI), 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 (called AutoODI) scoring these reviews on a composite ODI score. Presence of ODI mentions manifested itself not only at micro-level (companies scoring high in ODI suffered from low stock growth) but also at macro-level (states hosting these companies were associated with high depression rates, talent shortage, and economic deprivation). This new way of applying AutoODI onto company reviews offers both theoretical implications for the literature in computational social science, occupational health and economic geography, and practical implications for companies and policy makers.

Body-focused repetitive behaviors (BFRBs), like face-touching or skin-picking, are hand-driven behaviors which can damage one’s appearance, if not identified early and treated. Technology for automatic detection is still under-explored, with few previous works being limited to wearables with single modalities (e.g., motion). Here, we propose a multi-sensory approach combining motion, orientation, and heart rate sensors to detect BFRBs. We conducted a feasibility study in which participants (N=10) were exposed to BFRBs-inducing tasks, and analyzed 380 mins of signals1 under an extensive evaluation of sensing modalities, cross-validation methods, and observation windows. Our models achieved an AUC > 0.90 in distinguishing BFRBs, which were more evident in observation windows 5 mins prior to the behavior as opposed to 1-min ones. In a follow-up qualitative survey, we found that not only the timing of detection matters but also models need to be context-aware, when designing just-in-time interventions to prevent BFRBs.

The news you read is, of course, a highly individual choice and one for which substantial and successful news recommendation techniques have been developed. But as well as what news you read, the way you choose and read that news is also known to be highly individual. We propose a framework for extending the user profile of news readers with features of these interactions. The extensions are dynamic through monitoring an individual's reading and browsing activity. They include factors learned from the user's interaction log and also factors inferred from category level definitions contained in the framework. We report a study in which users' interaction logs with a news app are used to generate user profiles that are verified with self-reported questionnaire data about reading habits. We discuss the implications of our user modeling approach in news personalisation for both recommendation and user interface personalisation for news apps.

As news is increasingly accessed on smartphones and tablets, the need for personalising news app interactions is apparent. We report a series of three studies addressing key issues in the development of adaptive news app interfaces. We first surveyed users' news reading preferences and behaviours; analysis revealed three primary types of reader. We then implemented and deployed an Android news app that logs users' interactions with the app. We used the logs to train a classifier and showed that it is able to reliably recognise a user according to their reader type. Finally we evaluated alternative, adaptive user interfaces for each reader type. The evaluation demonstrates the differential benefit of the adaptation for different users of the news app and the feasibility of adaptive interfaces for news apps.

In this demonstration paper, we present an indoor positioning system developed for Android smartphones, coined Airplace. To infer the unknown user location we rely on ubiquitous WLANs and exploit Received Signal Strength (RSS) values from neighboring Access Points (AP) that are constantly monitored by the mobile devices under normal operation. Our system follows a mobile-based network-assisted architecture to eliminate the communication overhead and respect user privacy. In a typical scenario, when a user walks inside a building a smartphone client conducts a single communication with our Distribution Server to receive the RSS radiomap and is then able to position itself independently using the observed RSS values. Moreover, we have implemented an Android application to facilitate the collection of RSS values by users that may contribute their data to our system for constructing and updating the radiomap through crowdsourcing1. We will demonstrate the real-time positioning capabilities of the system during the conference by allowing attendees to carry an Android tablet in order to view their position on a floorplan map, while walking around inside the demo area (interactive scenario). Moreover, we will illustrate how to evaluate the performance of different positioning algorithms using profiled data in a trace-driven scenario. Our objective is to highlight the effectiveness and applicability of our system and at the same time the participants will be able to appreciate the potential of indoor location-oriented services and applications.

In this demo paper, we present the Airplace indoor positioning platform developed for Android smartphones [1]. Airplace relies on existing WLAN infrastructure and exploits Received Signal Strength (RSS) values from neighboring Access Points (AP) to infer the unknown user location. Our system utilizes a number of RSS fingerprints collected a priori to build the so-called radiomap. Location is then estimated by finding the best match between the currently measured fingerprint and fingerprints in the radiomap [2].