When the Codec Hallucinates: User Perceptions of Miscompressed ImagesPeople exchange images every day. New methods for image compression leverage neural networks to save bandwidth, but they can undermine the semantic integrity. The term miscompression refers to unintended semantic changes of image details, introduced by generative AI during neural (de)compression. Although prior work has speculated about the resulting risks, no empirical evidence exists on how people perceive these novel compression artifacts. In this study, 115 human subjects compared original images with conventionally compressed, neurally compressed, and miscompressed images. Participants perceive that miscompressions elevate the risk of misunderstandings when communicating with images. They also frequently attribute miscompressions to intentional editing, whereas conventional JPEG artifacts are more often recognized as distortions. This paper proposes a method to study this new phenomenon, provides the first empirical evidence of user perceptions of miscompressions, and derives implications for trust in images, as well as interface designs that mitigate the risk.2026NHNora Hofer et al.University of InnsbruckExplainable AI (XAI)Privacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
What Users Like and Don’t Like About Occupational Exoskeletons: Experiences and Implications From a Focus Group StudyOccupational exoskeletons are designed to support workers in strenuous tasks and to promote health, yet their implementation and use often present challenges due to the close interaction between wearer and device. This study explored user perceptions of occupational exoskeletons through qualitative focus groups conducted after participants had gained hands-on experience with 16 different devices in four-hour trials. Key findings highlight users’ feedback on system sound, design, and support, movement restriction and wearer comfort, and underscore the important role of bodily sensations–alongside factors, such as usability and appearance–in exoskeleton user experience. A central discovery was the existence of conflicts between user preferences, for instance, between light-weight designs and effective user support. Based on these insights, we highlight implications for human-centered design of exoskeletons and aim to inspire further research within the human-computer interaction community.2026SSSandra Maria Siedl et al.Johannes Kepler University LinzForce Feedback & Pseudo-Haptic WeightHaptic WearablesHuman Pose & Activity RecognitionCHI
Using Nonverbal Cues in Empathic Multi-Modal LLM-Driven Chatbots for Mental Health SupportDespite their popularity in providing digital mental health support, mobile conversational agents primarily rely on verbal input, which limits their ability to respond to emotional expressions. We therefore envision using the sensory equipment of today's devices to increase the nonverbal, empathic capabilities of chatbots. We initially validated that multi-modal LLMs (MLLM) can infer emotional expressions from facial expressions with high accuracy. In a user study (N=200), we then investigated the effects of such multi-modal input on response generation and perceived system empathy in emotional support scenarios. We found significant effects on cognitive and affective dimensions of linguistic expression in system responses, yet no significant increases in perceived empathy. Our research demonstrates the general potential of using nonverbal context to adapt LLM response behavior, providing input for future research on augmented interaction in empathic MLLM-based systems.2025MSMatthias Schmidmaier et al.Motion Sickness & Passenger ExperienceConversational ChatbotsHuman-LLM CollaborationMobileHCI
Ad-Blocked Reality: Evaluating User Perceptions of Content Blocking Concepts Using Extended RealityInspired by the concepts of diminishing reality and ad-blocking in browsers, this study investigates the perceived benefits and concerns of blocking physical, real-world content, particularly ads, through Extended Reality (XR). To understand how users perceive this concept, we first conducted a user study (N=18) with an ad-blocking prototype to gather initial insights. The results revealed a mixed willingness to adopt XR blockers, with participants appreciating aspects such as customizability, convenience, and privacy. Expected benefits included enhanced focus and reduced stress, while concerns centered on missing important information and increased feelings of isolation. Hence, we investigated the user acceptance of different ad-blocking visualizations through a follow-up online survey (N=120), comparing six concepts based on related work. The results indicated that the XR ad-blocker visualizations play a significant role in how and for what kinds of advertisements such a concept might be used, paving the path for future feedback-driven prototyping.2025CKChristopher Katins et al.HU BerlinPrivacy by Design & User ControlSocial Platform Design & User BehaviorCHI
Navigating the Virtual Gaze: Social Anxiety's Role in VR proxemicsFor individuals with Social Anxiety (SA), interacting with others can be a challenging experience, a concern that extends into the virtual world. While technology has made significant strides in creating more realistic virtual human agents (VHA), the interplay of gaze and interpersonal distance when interacting with VHAs is often neglected. This paper investigates the effect of dynamic and static Gaze animations in VHAs on interpersonal distance and their relation to SA. A Bayesian analysis shows that static centered and dynamic centering gaze led participants to stand closer to VHAs than static averted and dynamic averting gaze, respectively. In the static gaze conditions, this pattern was found to be reversed in SA: participants with higher SA kept larger distances for static-centered gaze than for averted gaze VHAs. These findings update theory, elucidate how nuanced interactions with VHAs must be designed, and offer renewed guidelines for pleasant VHA interaction design.2024BMBeatriz Mello et al.University of MinhoSocial & Collaborative VRImmersion & Presence ResearchCHI
HIFuzz: Human Interaction Fuzzing for Small Unmanned Aerial VehiclesSmall Unmanned Aerial Systems (sUAS) must meet rigorous safety standards when deployed in high-stress emergency response scenarios; however many reported accidents have involved humans in the loop. In this paper, we, therefore, present the HiFuzz testing framework, which uses fuzz testing to identify system vulnerabilities associated with human interactions. HiFuzz includes three distinct levels that progress from a low-cost, limited-fidelity, large-scale, no-hazard environment, using fully simulated Proxy Human Agents, via an intermediate level, where proxy humans are replaced with real humans, to a high-stakes, high-cost, real-world environment. Through applying HiFuzz to an autonomous multi-sUAS system-under-test, we show that each test level serves a unique purpose in revealing vulnerabilities and making the system more robust with respect to human mistakes. While HiFuzz is designed for testing sUAS systems, we further discuss its potential for use in other Cyber-Physical Systems.2024TCTheodore Chambers et al.University of Notre DameDrone Interaction & ControlTeleoperation & TelepresenceCHI
Perceived Empathy of Technology Scale (PETS): Measuring Empathy of Systems Toward the UserAffective computing improves rapidly, allowing systems to process human emotions. This enables systems such as conversational agents or social robots to show empathy toward users. While there are various established methods to measure the empathy of humans, there is no reliable and validated instrument to quantify the perceived empathy of interactive systems. Thus, we developed the Perceived Empathy of Technology Scale (PETS) to assess and compare how empathic users perceive technology. We followed a standardized multi-phase process of developing and validating scales. In total, we invited 30 experts for item generation, 324 participants for item selection, and 396 additional participants for scale validation. We developed our scale using 22 scenarios with opposing empathy levels, ensuring the scale is universally applicable. This resulted in the PETS, a 10-item, 2-factor scale. The PETS allows designers and researchers to evaluate and compare the perceived empathy of interactive systems rapidly.2024MSMatthias Schmidmaier et al.LMU MunichAgent Personality & AnthropomorphismSocial Robot InteractionCHI
Show me a "Male Nurse"! How Gender Bias is Reflected in the Query Formulation of Search Engine UsersBiases in algorithmic systems have led to discrimination against historically disadvantaged groups, including the reinforcement of outdated gender stereotypes. While a substantial body of research addresses biases in algorithms and underlying data, in this work, we study if and how users themselves reflect these biases in their interactions with systems, which expectedly leads to the further manifestation of biases. More specifically, we investigate the replication of stereotypical gender representations by users in formulating online search queries. Following prototype theory, we define the disproportionate mention of the gender that does not conform to the prototypical representative of a searched domain (e.g., “male nurse”) as an indication of bias. In a pilot study with 224 US participants and a main study with 400 UK participants, we find clear evidence of gender biases in formulating search queries. We also report the effects of an educative text on user behaviour and highlight the wish of users to learn about bias-mitigating strategies in their interactions with search engines.2023SKSimone Kopeinik et al.Know-CenterAI Ethics, Fairness & AccountabilityAlgorithmic Fairness & BiasCHI
Bits Under the Mattress: Understanding Different Risk Perceptions and Security Behaviors of Crypto-Asset UsersCrypto-assets are unique in tying financial wealth to the secrecy of private keys. Prior empirical work has attempted to study end-user security from both technical and organizational perspectives. However, the link between individuals' risk perceptions and security behavior was often obscured by the heterogeneity of the subjects in small samples. This paper contributes quantitative results from a survey of 395 crypto-asset users recruited by a novel combination of deep and broad sampling. The analysis accounts for heterogeneity with a new typology that partitions the sample in three robust clusters - cypherpunks, hodlers, and rookies - using five psychometric constructs. The constructs originate from established behavioral theories with items purposefully adapted to the domain. We demonstrate the utility of this typology in better understanding users' characteristics and security behaviors. These insights inform the design of crypto-asset solutions, guide risk communication, and suggest directions for future digital currencies.2021SASvetlana Abramova et al.University of InnsbruckPrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Webcam Covering as Planned BehaviorMost of today's laptops come with an integrated webcam placed above the screen to enable video conferencing. Due to the risk of webcam spying attacks, some laptop users seem to be concerned about their privacy and seek protection by covering the webcam. This paper is the first to investigate personal characteristics and beliefs of users with and without webcam covers by applying the Theory of Planned Behavior. We record the privacy behavior of 180 users, develop a path model, and analyze it by applying Partial Least Squares. The analysis indicates that privacy concerns do not significantly influence users' decision to use a webcam cover. Rather, this behavior is influenced by users' attitudes, social environment, and perceived control over protecting privacy. Developers should take this as a lesson to design privacy enhancing technologies which are convenient, verifiably effective and endorsed by peers.2018DMDominique Machuletz et al.University of MünsterPrivacy by Design & User ControlPasswords & AuthenticationPrivacy Perception & Decision-MakingCHI