Papers

Civil Data Disobedience: Navigating Data Interaction Challenges in Human Rights Defense Organizations

The increasing digitalization of society has intensified the importance of secure and effective data management. For human rights defender organizations, these demands are complicated by scarce resources and risks of surveillance and online harassment. Simultaneously, regulatory frameworks such as the GDPR shape how these organizations are required to handle data. This paper examines how human rights defender organizations in Sweden navigate data practices, focusing on their strategies, challenges, and the effects of legal requirements. Drawing on critical data literacy and data feminist perspectives, we conceptualize data literacy as the ability to interpret and act on data in relation to its social and political effects. We show that limited resources significantly constrain organizations’ ability to adopt robust data practices. Nonetheless, data remains crucial for their advocacy and support of marginalized communities. We contribute to HCI by showing how human rights defender organizations develop situated, feminist forms of critical data literacy that challenge dominant assumptions about security, compliance, and good data practice.

Visualization of Tracking Uncertainty in AR-based Surgical Guidance

Uncertainty caused by instrument tracking errors affects critical tasks such as surgery assisted by Augmented Reality (AR) guidance. This work investigates whether visualizing such uncertainty can improve task performance, trust, and confidence. We present four visualization techniques: Cone, Circle, Gauge, and Color. A two-part study evaluated these techniques on a surgical drilling task, first with 24 non-professional participants and then with 4 professional surgeons. Results indicate that uncertainty visualization improved drilling accuracy by 24% but increased task time by 76%. It also enhanced user confidence and trust in the system, with Cone and Circle as the most preferred visualizations. Based on our findings, we discuss design recommendations for integrating uncertainty visualization into AR-based surgical systems. This work paves the way for a higher success rate in surgical procedures.

Investigating the Effects of Eco-Friendly Service Options on Rebound Behavior in Ride-Hailing

Eco-friendly service options (EFSOs) aim to reduce personal carbon emissions, yet their eco-friendly framing may permit increased consumption, weakening their intended impact. Such rebound effects remain underexamined in HCI, including how common eco-feedback approaches shape them. We investigate this in an online within-subjects experiment (N=75) in a ride-hailing context. Participants completed 10 trials for five conditions (No EFSO, EFSO - Minimal, EFSO - CO2 Equivalency, EFSO - Gamified, EFSO - Social), yielding 50 choices between walking and ride-hailing for trips ranging from 0.5mi - 2.0mi (≈ 0.80km - 3.22km). We measured how different EFSO variants affected ride-hailing uptake relative to a No EFSO baseline. EFSOs lacking explicit eco-feedback metrics increased ride-hailing uptake, and qualitative responses indicate that EFSOs can make convenience-driven choices more permissible. We conclude with implications for designing EFSOs that begin to take rebound effects into account.

"I Know You Are Discriminatory!": Automated Substantiating for Individual Fairness Auditing of AI Systems

Artificial intelligence (AI) systems are playing an increasingly crucial role in people's lives, and their frequent unfair behaviors raise concerns about fairness. To unveil the unfairness in AI systems, researchers conduct fairness auditing on these systems. However, existing fairness auditing works often focus on group fairness while ignoring discriminatory phenomena among individuals. To unearth discriminatory phenomena against individuals within AI systems, this paper proposes an individual fairness auditing framework, termed "substantiating", which can identify discrimination instances within AI systems by constructing individual samples. To construct these samples for substantiating, auditors often have to rely on subjective prior knowledge, lacking guidelines on how to construct unfair samples. To address this issue, this paper introduces two categories of automated sample generation methods that can rapidly find unfair samples within a limited number of queries to the system and demonstrate their effectiveness through experiments. This paper evaluates the proposed auditing framework among three categories of stakeholders in AI fairness: auditors, AI model developers, and non-technical personnel. The research findings point out their demand for individual fairness audits of AI systems and highlight how the framework supports a reliable and convenient individual fairness audit.

The Collaborative Work of Stewardship in Waste Management in Multi-tenant Apartment Buildings

This paper examines the collaborative work of residents, housing associations, and property owners, in a multi-apartment housing complex, to manage household waste. Framed within the feminist ecological perspective of digital environmental stewardship - that is, how diverse actors, motivations, and capacities producing care for the environment that can be digitally mediated - we unpack how the many actors involved work together to keep waste in place, maintain the local waste system, and call on `responsibility' as a means to produce sustainable actions and accountability. We frame these practices of waste management within the mundane work of sociotechnical innovation. Borrowing from Jackson's notion of repair work, we weave together an argument for the novel and valuable contribution to sustainability research of CSCW approaches grounded in the everyday contingent emergencies of environmental care. We argue for approaches to sustainability that reflect the work to maintain sustainability ––not just produce it-- and the `good enough', a locally and reflexively produced equilibrium between maintenance and repair, which can frame the design of sociotechnical interventions mediating practices of waste management.

Mind Games! Exploring the Impact of Dark Patterns in Mixed Reality Scenarios

Mixed Reality (MR) integrates virtual objects with the real world, offering potential but raising concerns about misuse through dark patterns. This study explored the effects of four dark patterns, adapted from prior research, and applied to MR across three targets: places, products, and people. In a two-factorial within-subject study with 74 participants, we analyzed 13 videos simulating MR experiences during a city walk. Results show that all dark patterns significantly reduced user comfort, increased reactance, and decreased the intention to use MR glasses, with the most disruptive effects linked to personal or monetary manipulation. Additionally, the dark patterns of Emotional and Sensory Manipulation and Hiding Information produced similar impacts on the user in MR, suggesting a re-evaluation of current classifications to go beyond deceptive design techniques. Our findings highlight the importance of developing ethical design guidelines and tools to detect and prevent dark patterns as immersive technologies continue to evolve.

Light My Way. Developing and Exploring a Multimodal Interface to Assist People With Visual Impairments to Exit Highly Automated Vehicles

The introduction of Highly Automated Vehicles (HAVs) has the potential to increase the independence of blind and visually impaired people (BVIPs). However, ensuring safety and situation awareness when exiting these vehicles in unfamiliar environments remains challenging. To address this, we conducted an interactive workshop with N=5 BVIPs to identify their information needs when exiting an HAV and evaluated three prior-developed low-fidelity prototypes. The insights from this workshop guided the development of PathFinder, a multimodal interface combining visual, auditory, and tactile modalities tailored to BVIP's unique needs. In a three-factorial within-between-subject study with N=16 BVIPs, we evaluated PathFinder against an auditory-only baseline in urban and rural scenarios. PathFinder significantly reduced mental demand and maintained high perceived safety in both scenarios, while the auditory baseline led to lower perceived safety in the urban scenario compared to the rural one. Qualitative feedback further supported PathFinder's effectiveness in providing spatial orientation during exiting.

Encounter with the Giants: Understanding Interaction with Large-scale Inflatable Soft Robots

Soft robots, constructed from compliant materials, offer unique flexibility and adaptability. However, most research has focused on small-scale interactions, leaving the potential of large-scale soft robots largely unexplored. This research explores how humans engage with inflatable soft robots that are large in size and created for fun and artistic expression. We conducted 22 hours of video analysis (N=30) and thematic interviews (N=20) to understand user engagement and explore their motivations. Our findings revealed a range of interactions, from delicate touches to immersive full-body engagement, driven by trust, safety, and emotional connection. Participants frequently compared the robots to peaceful creatures like plants and sea animals, fostering playful and therapeutic interactions. These insights highlight the potential of giant soft robots in enhancing emotional well-being, therapeutic applications, and immersive experiences. This paper aims to inspire future designs that leverage the unique attributes of large-scale soft robots for trust-centered, interactive human-robot relationships.

PlanTogether: Facilitating AI Application Planning Using Information Graphs and Large Language Models

In client-AI expert collaborations, the planning stage of AI application development begins from the client; a client outlines their needs and expectations while assessing available resources (pre-collaboration planning). Despite the importance of pre-collaboration plans for discussions with AI experts for iteration and development, the client often fails to reflect their needs and expectations into a concrete actionable plan. To facilitate pre-collaboration planning, we introduce PlanTogether, a system that generates tailored client support using large language models and a Planning Information Graph, whose nodes and edges represent information in the plan and the information dependencies. Using the graph, the system links and presents information that guides client's reasoning; it provides tips and suggestions based on relevant information and displays an overview to help understand the progression through the plan. A user study validates the effectiveness of PlanTogether in helping clients navigate information dependencies and write actionable plans reflecting their domain expertise.

When Do We Feel Present in a Virtual Reality? Towards Sensitivity and User Acceptance of Presence Questionnaires

Presence is an important and widely used metric to measure the quality of virtual reality (VR) applications. Given the multifaceted and subjective nature of presence, the most common measures for presence are questionnaires. But there is little research on their validity regarding specific presence dimensions and their responsiveness to differences in perception among users. We investigated four presence questionnaires (SUS, PQ, IPQ, Bouchard) on their responsiveness to intensity variations of known presence dimensions and asked users about their consistency with their experience. Therefore, we created five VR scenarios that were designed to emphasize a specific presence dimension. Our findings showed heterogeneous sensitivity of the questionnaires dependent on the different dimensions of presence. This highlights a context-specific suitability of presence questionnaires. The questionnaires' sensitivity was further stated as lower than actually perceived. Based on our findings, we offer guidance on selecting these questionnaires based on their suitability for particular use cases.

Scrolling in the Deep: Analysing Contextual Influences on Intervention Effectiveness during Infinite Scrolling on Social Media

Infinite scrolling on social media platforms is designed to encourage prolonged engagement, leading users to spend more time than desired, which can provoke negative emotions. Interventions to mitigate infinite scrolling have shown initial success, yet users become desensitized due to the lack of contextual relevance. Understanding how contextual factors influence intervention effectiveness remains underexplored. We conducted a 7-day user study (N=72) investigating how these contextual factors affect users' reactance and responsiveness to interventions during infinite scrolling. Our study revealed an interplay, with contextual factors such as being at home, sleepiness, and valence playing significant roles in the intervention's effectiveness. Low valence coupled with being at home slows down the responsiveness to interventions, and sleepiness lowers reactance towards interventions, increasing user acceptance of the intervention. Overall, our work contributes to a deeper understanding of user responses toward interventions and paves the way for developing more effective interventions during infinite scrolling.

Bumpy Ride? Understanding the Effects of External Forces on Spatial Interactions in Moving Vehicles

As the use of Head-Mounted Displays in moving vehicles increases, passengers can immerse themselves in visual experiences independent of their physical environment. However, interaction methods are susceptible to physical motion, leading to input errors and reduced task performance. This work investigates the impact of G-forces, vibrations, and unpredictable maneuvers on 3D interaction methods. We conducted a field study with 24 participants in both stationary and moving vehicles to examine the effects of vehicle motion on four interaction methods: (1) Gaze\&Pinch, (2) DirectTouch, (3) Handray, and (4) HeadGaze. Participants performed selections in a Fitts' Law task. Our findings reveal a significant effect of vehicle motion on interaction accuracy and duration across the tested combinations of Interaction Method $\times$ Road Type $\times$ Curve Type. We found a significant impact of movement on throughput, error rate, and perceived workload. Finally, we propose future research considerations and recommendations on interaction methods during vehicle movement.

An Evaluation of Spatial Anchoring to position AR Guidance in Arthroscopic Surgery

This work examines spatial anchoring strategies to position augmented reality guidance during surgery. We consider three strategies: anchoring to the Patient, the surgical Tool, and the Surgeon's head. These strategies were evaluated in a first experiment involving 24 non-professional participants, using two guidance techniques: 3D Trajectory and 2D Crosshair. For 3D Trajectory, Patient and Tool anchoring were more precise than Surgeon anchoring, and Patient anchoring was the most preferred. For 2D Crosshair, no significant effect of anchoring strategies on precision was observed. However, participants preferred Patient and Surgeon anchoring. A second experiment with 6 surgeons confirmed the first experiment's results. For 3D trajectory, Tool anchoring proved more precise than Patient anchoring, despite surgeons' preference for Patient anchoring. These findings contribute to empirical evidence for the design of surgical AR guidance, with potential applications for similar, less critical tasks.

TelePulse: Enhancing the Teleoperation Experience through Biomechanical Simulation-Based Electrical Muscle Stimulation in Virtual Reality

This paper introduces TelePulse, a system integrating biomechanical simulation with electrical muscle stimulation (EMS) to provide precise haptic feedback for robot teleoperation tasks in virtual reality (VR). TelePulse has two components: a physical simulation part that calculates joint torques based on real-time force data from remote manipulators, and an electrical stimulation part that converts these torques into muscle stimulation. Two experiments were conducted to evaluate the system. The first experiment assessed the accuracy of EMS generated through biomechanical simulations by comparing it with electromyography (EMG) data during force-directed tasks, while the second experiment evaluated the impact of TelePulse on teleoperation performance during sanding and drilling tasks. The results suggest that TelePulse provided more accurate stimulation across all arm muscles, thereby enhancing task performance and user experience in the teleoperation environment. In this paper, we discuss the effect of TelePulse on teleoperation, its limitations, and areas for future improvement.

Stretch Gaze Targets Out: Experimenting with Target Sizes for Gaze-Enabled Interfaces on Mobile Devices

Users hold their mobile phones at varying distances depending on their posture, the application being used, and the task's nature. Without considering such variation when designing UI target sizes limits the applicability of gaze selection for everyday interaction with mobile devices. Towards this end, we conducted a user study (N=24) to investigate the implications of different target sizes and viewing across different screen regions. While larger targets generally improve accuracy and decrease precision, accuracy is significantly higher in the horizontal than in the vertical direction. This subsequently led us to find that increasing the tracking area in the vertical direction only, while maintaining the same visual target size, significantly improves accuracy. This suggests that visually smaller targets with larger vertical tracking areas enhance accuracy. Based on our results, we present concrete design guidelines for developers to optimise target sizes on gaze-enabled mobile devices to improve accuracy across varying user-to-screen distances.

Speculating Deaf Tech: Reimagining Technologies Centering Deaf People

This deaf-led work critically explores Deaf Tech, challenging conventional understandings of technologies 'for' deaf people as merely assistive and accessible, since these understandings are predominantly embedded in medical and audist ideologies. By employing participatory speculative workshops, deaf participants from different European countries envisioned technologies on Eyeth - a mythical planet inhabited by deaf people - centered on their perspectives and curiosities. The results present a series of alternative socio-technical narratives that illustrate qualitative aspects of technologies desired by deaf people. This study advocates for expanding the scope of deaf technological landscapes, emphasizing the needs of establishing deaf-centered HCI, including the development of methods and concepts that truly prioritize deaf experiences in the design of technologies intended for their use.

Real-Time Adaptive Industrial Robots: Improving Safety And Comfort In Human-Robot Collaboration

Industrial robots become increasingly prevalent, resulting in a growing need for intuitive, comforting human-robot collaboration. We present a user-aware robotic system that adapts to operator behavior in real time while non-intrusively monitoring physiological signals to create a more responsive and empathetic environment. Our prototype dynamically adjusts robot speed and movement patterns to proxemics while measuring operator pupil dilation. Our user study compares this adaptive system to a non-adaptive counterpart, and demonstrates that the adaptive system significantly reduces both perceived and physiologically measured cognitive load while enhancing usability. Participants reported increased feelings of comfort, safety, trust, and a stronger sense of collaboration when working with the adaptive robot. This highlights the potential of integrating real-time physiological data into human-robot interaction paradigms. This novel approach creates more intuitive and collaborative industrial environments where robots effectively ’read’ and respond to human cognitive states, and we feature all data and code for future use.

Fly Away: Evaluating the Impact of Motion Fidelity on Optimized User Interface Design via Bayesian Optimization in Automated Urban Air Mobility Simulations

Automated Urban Air Mobility (UAM) can improve passenger transportation and reduce congestion, but its success depends on passenger trust. While initial research addresses passengers' information needs, questions remain about how to simulate air taxi flights and how these simulations impact users and interface requirements. We conducted a between-subjects study (N=40), examining the influence of motion fidelity in Virtual-Reality-simulated air taxi flights on user effects and interface design. Our study compared simulations with and without motion cues using a 3-Degrees-of-Freedom motion chair. Optimizing the interface design across six objectives, such as trust and mental demand, we used multi-objective Bayesian optimization to determine the most effective design trade-offs. Our results indicate that motion fidelity decreases users' trust, understanding, and acceptance, highlighting the need to consider motion fidelity in future UAM studies to approach realism. However, minimal evidence was found for differences or equality in the optimized interface designs, suggesting personalized interface designs.

Efficient Management of LLM-Based Coaching Agents' Reasoning While Maintaining Interaction Quality and Speed

LLM-based agents improve upon standalone LLMs, which are optimized for immediate intent-satisfaction, by allowing the pursuit of more extended objectives, such as helping users over the long term. To do so, LLM-based agents need to reason before responding. For complex tasks like personalized coaching, this reasoning can be informed by adding relevant information at key moments, shifting it in the desired direction. However, the pursuit of objectives beyond interaction quality may compromise this very quality. Moreover, as the depth and informativeness of reasoning increase, so do the number of tokens required, leading to higher latency and cost. This study investigates how an LLM-based coaching agent can adjust its reasoning depth using a discrepancy mechanism that signals how much reasoning effort to allocate based on how well the objective is being met. Our discrepancy-based mechanism constrains reasoning to better align with alternative objectives, reducing cost roughly tenfold while minimally impacting interaction quality.

AutoTherm: A Dataset and Benchmark for Thermal Comfort Estimation Indoors and in Vehicles