Investigating Users' Decision-making for Data Privacy Controls in the Context of Internet of Things (IoT) Devices Using an Incentive-compatible Lottery StudyWhile companies are increasingly moving towards the ‘pay for privacy’ model, it is unclear how consumers make privacy decisions under this model. Toward that, we conducted an incentive-compatible lottery study on Prolific to understand the factors behind users’ choice to have additional data privacy controls. With 265 United States participants across two device risk conditions (High-risk: camera vs. Low-risk: light bulb) and three cash conditions ($9.99 vs. $19.99 vs. $29.99), results reveal that device risk and cash offerings influence participants’ lottery choice. We further observed an interaction effect between participants’ technical literacy and cash option. Specifically, technical participants chose the data privacy controls instead of cash at a higher rate when the cash condition was $29.99. In contrast, less technical participants favored the privacy option at a higher rate when the cash condition was $9.99. Implications of our findings for user data privacy are discussed in the paper.2025EHEhsan Ul Haque et al.University of Connecticut, Computer Science and EngineeringPrivacy by Design & User ControlPrivacy Perception & Decision-MakingIoT Device PrivacyCHI
The Role of Inclusion, Control, and Ownership in Workplace AI-Mediated CommunicationGiven large language models' (LLMs) increasing integration into workplace software, it is important to examine how biases in the models may impact workers. For example, stylistic biases in the language suggested by LLMs may cause feelings of alienation and result in increased labor for individuals or groups whose style does not match. We examine how such writer-style bias impacts inclusion, control, and ownership over the work when co-writing with LLMs. In an online experiment, participants wrote hypothetical job promotion requests using either hesitant or self-assured autocomplete suggestions from an LLM and reported their subsequent perceptions. We found that the style of the AI model did not impact perceived inclusion. However, individuals with higher perceived inclusion did perceive greater agency and ownership, an effect more strongly impacting participants of minoritized genders. Feelings of inclusion mitigated a loss of control and agency when accepting more AI suggestions.2024KKKowe Kadoma et al.Cornell UniversityHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityAlgorithmic Fairness & BiasCHI
The Value, Benefits, and Concerns of Generative AI-Powered Assistance in WritingRecent advances in generative AI technologies like large language models raise both excitement and concerns about the future of human-AI co-creation in writing. To unpack people’s attitude towards and experience with generative AI-powered writing assistants, in this paper, we conduct an experiment to understand whether and how much value people attach to AI assistance, and how the incorporation of AI assistance in writing workflows changes people’s writing perceptions and performance. Our results suggest that people are willing to forgo financial payments to receive writing assistance from AI, especially if AI can provide direct content generation assistance and the writing task is highly creative. Generative AI-powered assistance is found to offer benefits in increasing people’s productivity and confidence in writing. However, direct content generation assistance offered by AI also comes with risks, including decreasing people’s sense of accountability and diversity in writing. We conclude by discussing the implications of our findings.2024ZLZiqing Li et al.Purdue universityGenerative AI (Text, Image, Music, Video)AI-Assisted Creative WritingCHI
Predicting Symptom Improvement During Depression Treatment Using Sleep Sensory Data"Depression is a serious mental illness. The current best guideline in depression treatment is closely monitoring patients and adjusting treatment as needed. Close monitoring of patients through physician-administered follow-ups or self-administered questionnaires, however, is difficult in clinical settings due to high cost, lack of trained professionals, and burden to the patients. Sensory data collected from mobile devices has been shown to provide a promising direction for long-term monitoring of depression symptoms. Most existing studies in this direction, however, focus on depression detection; the few studies that are on predicting changes in depression are not in clinical settings. In this paper, we investigate using one type of sensory data, sleep data, collected from wearables to predict improvement of depression symptoms over time after a patient initiates a new pharmacological treatment. We apply sleep trend filtering to noisy sleep sensory data to extract high-level sleep characteristics and develop a family of machine learning models that use simple sleep features (mean and variation of sleep duration) to predict symptom improvement. Our results show that using such simple sleep features can already lead to validation F1 score up to 0.68, indicating that using sensory data for predicting depression improvement during treatment is a promising direction." https://doi.org/10.1145/36109322023CSChinmaey Shende et al.Mental Health Apps & Online Support CommunitiesSleep & Stress MonitoringUbiComp
Cross-Modality Graph-based Language and Sensor Data Co-Learning of Human-Mobility Interaction"Learning the human--mobility interaction (HMI) on interactive scenes (e.g., how a vehicle turns at an intersection in response to traffic lights and other oncoming vehicles) can enhance the safety, efficiency, and resilience of smart mobility systems (e.g., autonomous vehicles) and many other ubiquitous computing applications. Towards the ubiquitous and understandable HMI learning, this paper considers both "spoken language" (e.g., human textual annotations) and "unspoken language" (e.g., visual and sensor-based behavioral mobility information related to the HMI scenes) in terms of information modalities from the real-world HMI scenarios. We aim to extract the important but possibly implicit HMI concepts (as the named entities) from the textual annotations (provided by human annotators) through a novel human language and sensor data co-learning design. To this end, we propose CG-HMI, a novel Cross-modality Graph fusion approach for extracting important Human-Mobility Interaction concepts from co-learning of textual annotations as well as the visual and behavioral sensor data. In order to fuse both unspoken and spoken "languages", we have designed a unified representation called the human--mobility interaction graph (HMIG) for each modality related to the HMI scenes, i.e., textual annotations, visual video frames, and behavioral sensor time-series (e.g., from the on-board or smartphone inertial measurement units). The nodes of the HMIG in these modalities correspond to the textual words (tokenized for ease of processing) related to HMI concepts, the detected traffic participant/environment categories, and the vehicle maneuver behavior types determined from the behavioral sensor time-series. To extract the inter- and intra-modality semantic correspondences and interactions in the HMIG, we have designed a novel graph interaction fusion approach with differentiable pooling-based graph attention. The resulting graph embeddings are then processed to identify and retrieve the HMI concepts within the annotations, which can benefit the downstream human-computer interaction and ubiquitous computing applications. We have developed and implemented CG-HMI into a system prototype, and performed extensive studies upon three real-world HMI datasets (two on car driving and the third one on e-scooter riding). We have corroborated the excellent performance (on average 13.11% higher accuracy than the other baselines in terms of precision, recall, and F1 measure) and effectiveness of CG-HMI in recognizing and extracting the important HMI concepts through cross-modality learning. Our CG-HMI studies also provide real-world implications (e.g., road safety and driving behaviors) about the interactions between the drivers and other traffic participants." https://doi.org/10.1145/36109042023MTMahan Tabatabaie et al.V2X (Vehicle-to-Everything) Communication DesignContext-Aware ComputingUbiquitous ComputingUbiComp
Naturalistic E-Scooter Maneuver Recognition with Federated Contrastive Rider Interaction Learning"Smart micromobility, particularly the electric (e)-scooters, has emerged as an important ubiquitous mobility option that has proliferated within and across many cities in North America and Europe. Due to the fast speed (say, ~15km/h) and ease of maneuvering, understanding how the micromobility rider interacts with the scooter becomes essential for the e-scooter manufacturers, e-scooter sharing operators, and rider communities in promoting riding safety and relevant policy or regulations. In this paper, we propose FCRIL, a novel Federated maneuver identification and Contrastive e-scooter Rider Interaction Learning system. FCRIL aims at: (i) understanding, learning, and identifying the e-scooter rider interaction behaviors during naturalistic riding (NR) experience (without constraints on the data collection settings); and (ii) providing a novel federated maneuver learning model training and contrastive identification design for our proposed rider interaction learning (RIL). Towards the prototype and case studies of FCRIL, we have harvested an NR behavior dataset based on the inertial measurement units (IMUs), e.g., accelerometer and gyroscope, from the ubiquitous smartphones/embedded IoT devices attached to the e-scooters. Based on the harvested IMU sensor data, we have conducted extensive data analytics to derive the relevant rider maneuver patterns, including time series, spectrogram, and other statistical features, for the RIL model designs. We have designed a contrastive RIL network which takes in these maneuver features with class-to-class differentiation for comprehensive RIL and enhanced identification accuracy. Furthermore, to enhance the dynamic model training efficiency and coping with the emerging micromobility rider data privacy concerns, we have designed a novel asynchronous federated maneuver learning module, which asynchronously takes in multiple sets of model gradients (e.g., based on the IMU data from the riders' smartphones) for dynamic RIL model training and communication overhead reduction. We have conducted extensive experimental studies with different smartphone models and stand-alone IMU sensors on the e-scooters. Our experimental results have demonstrated the accuracy and effectiveness of FCRIL in learning and recognizing the e-scooter rider maneuvers. https://dl.acm.org/doi/10.1145/3570345"2023MTMahan Tabatabaie et al.Micromobility (E-bike, E-scooter) InteractionHuman Pose & Activity RecognitionUbiComp
"I Want to Figure Things Out": Supporting Exploration in Navigation for People with Visual ImpairmentsNavigation assistance systems (NASs) aim to help visually impaired people (VIPs) navigate unfamiliar environments. Most of today’s NASs support VIPs via turn-by-turn navigation, but a growing body of work highlights the importance of exploration as well. It is unclear, however, how NASs should be designed to help VIPs explore unfamiliar environments. In this paper, we perform a qualitative study to understand VIPs' information needs and challenges with respect to exploring unfamiliar environments, with the aim of informing the design of NASs that support exploration. Our findings reveal the types of spatial information that VIPs need as well as factors that affect VIPs' information preferences. We also discover specific challenges that VIPs face that future NASs can address such as orientation and mobility education and collaborating effectively with others. We present design implications for NASs that support exploration, and we identify specific research opportunities and discuss open socio-technical challenges for making such NASs possible. We conclude by reflecting on our study procedure to inform future approaches in research on ethical considerations that may adopted while interacting with the broader VIP community.2023GJGaurav Jain et al.AccessibilityCSCW
The Nuanced Nature of Trust and Privacy Control Adoption in the Context of GoogleThis paper investigates how trust towards service providers and the adoption of privacy controls belonging to two specific purposes (control over “sharing” vs. “usage” of data) vary based on users’ technical literacy. Towards that, we chose Google as the context and conducted an online survey across 209 Google users. Our results suggest that integrity and benevolence perceptions toward Google are significantly lower among technical participants than non-technical participants. While trust perceptions differ between non-technical adopters and non-adopters of privacy controls, no such difference is found among the technical counterparts. Notably, among the non-technical participants, the direction of trust affecting privacy control adoption is observed to be reversed based on the purpose of the controls. Using qualitative analysis, we extract trust-enhancing and dampening factors contributing to users' trusting beliefs towards Google's protection of user privacy. The implications of our findings for the design and promotion of privacy controls are discussed in the paper.2023EHEhsan Ul Haque et al.University of ConnecticutPrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Distance Education During the COVID-19 Shutdown: A Process Model of Online Learner Readiness, Experiences and Feelings of Learning The COVID-19 pandemic that forced the closure of campuses in the spring of 2020 accelerated the diffusion of distance education in Universities worldwide. The need to shift to distance education without time to prepare or train teachers or students led to what has been called a crisis learning situation in what was far from a seamless transition. This article surveyed 200 students at a large university in the United States at the end of the spring semester in 2020 about their online learner readiness including access to technological infrastructure (computers and highspeed Internet access), computer self-efficacy, computer anxiety, and how that influenced positive and negative learning experiences and feelings about distance education and learning. A four-stage Structural Equation Model shows a detailed picture of the distance education process and suggests intervention points to improve its outcomes. Results suggest that access to technological infrastructure are necessary but not sufficient for successful distance education experiences and point to the critical importance of computer self-efficacy and anxiety in predicting positive (or negative) learning experiences, which lead to increased feelings of learning and the likelihood that students will choose distance education in the future.2022KNKristine L Nowak et al.Online Learning; Online LearningCSCW
News Informatics: Engaging Individuals with Data-Rich News Content through Interactivity in Source, Medium, and MessageThis paper introduces the concept of “news informatics” to refer to journalistic presentation of big data in online sites. For users to be engaged with such data-driven public information, it is important to incorporate interactive tools so that each person can extract personally relevant information. Drawing upon a communication model of interactivity, we designed a data-rich site with three different types of interactive features—namely, modality interactivity, message interactivity, and source interactivity—and empirically tested their relative and combined effects on user engagement and user experience with a 2 (modality) × 3 (source) × 2 (message) field experiment (N =166). Findings shed light on how interface designers, online news editors and journalists can maximize user engagement with data-rich news content. Certain interactivity combinations are found to be better than others, with a structural equation model (SEM) revealing the underlying theoretical mechanisms and providing implications for the design of news informatics.2022SSS. Shyam Sundar et al.The Pennsylvania State UniversityAutomated Driving Interface & Takeover DesignData StorytellingCHI
Trust and Anthropomorphism in Tandem: The Interrelated Nature of Automated Agent Appearance and Reliability in Trustworthiness PerceptionsAnthropomorphism in the design of interface agents is implicitly linked to increasing user trust and acceptance. However, the role of perceived anthropomorphism and perceived trustworthiness in trust appropriateness given a system's capabilities and limitations is unclear. We designed a 2 (reliability: low, high) x 3 (agent appearance: computer, avatar, human) between-subject study to observe how agent appearance influenced user perceptions of and reliance on an automated teammate in a collaborative image classification task. Trust appropriateness was characterized as the degree to which reliance matched an optimal level given the system's reliability. Although agent appearance did not significantly influence trust appropriateness, it did affect perceptions of trustworthiness, particularly for low reliability agents. Our results suggest that trust and anthropomorphism involve highly related, dynamic perceptions aimed at anticipating system behavior. Based on our findings, recommendations for future research on trust and anthropomorphism are discussed along with some design implications.2021TJTheodore Jensen et al.Agent Personality & AnthropomorphismExplainable AI (XAI)DIS
Do Integral Emotions Affect Trust? The Mediating Effect of Emotions on Trust in the Context of Human-Agent InteractionPrior efforts have noted the effect of reliability, risk, and degree of anthropomorphism on trust in the context of human-agent interaction. However, the effects of these factors on resulting emotions while interacting with autonomous agents and their influence on trust are not clear. Towards that, we designed a 2 (partner: automation/human) × 2 (risk: low/high) × 2 (reliability: low/high) between-group study to identify relevant discrete emotions and their (emotions') influences on users' trustworthiness perceptions (ability, integrity, and benevolence). The results identified four emotion factors (positive emotions, hostility, anxiety, and loneliness) related to human-agent interaction. Although the reliability condition affected all four emotion factors, the mediating effects of the emotion factors on reliability and trustworthiness perceptions relationships differed for the varying emotion factors. The implications of our findings for trust calibration in the context of designing interactive systems are discussed in the paper.2021MFMd Abdullah Al Fahim et al.Agent Personality & AnthropomorphismExplainable AI (XAI)AI-Assisted Decision-Making & AutomationDIS