Finding Understanding and Support: Navigating Online Communities to Share and Connect at the intersection of Abuse and Foster Care Experiences Many children in foster care experience trauma that is rooted in unstable family relationships. Other members of the foster care system like foster parents and social workers face secondary trauma. Drawing on 10 years of Reddit data, we used a mixed methods approach to analyze how different members of the foster care system find support and similar experiences at the intersection of two Reddit communities - foster care, and abuse. We found that users who cross the boundary between the two communities focus on trauma experiences specific to different roles in foster care. While representing a small number of users, cross-posters contribute heavily to both communities, and, compared to other community members, receive higher scores and more replies. We explore the roles boundary crossing users have both in the online community and in the context of foster care. Finally, we present design, practice, and policy recommendations that would support survivors of trauma find communities more suited to their personal experiences.2025TATawfiq Ammari et al.Recovering From a CrisisCSCW
Trustworthy by Design: The Viewer's Perspective on Trust in Data VisualizationDespite the importance of viewers' trust in data visualization, there is a lack of research on the viewers’ own perspective on their trust. In addition, much of the research on trust remains relatively theoretical and inaccessible for designers. This work aims to address this gap by conducting a qualitative study to explore how viewers perceive different data visualizations and how their perceptions impact their trust. Three dominant themes emerged from the data. First, users appeared to be consistent, listing similar rationale for their trust across different stimuli. Second, there were diverse opinions about what factors were most important to trust perception and about why the factors matter. Third, despite this disagreement, there were important trends to the factors that users reported as impactful. Finally, we leverage these themes to give specific and actionable guidelines for visualization designers to make more trustworthy visualizations.2025OMOen G McKinley et al.Washington University in St. Louis, Department of Computer Science and EngineeringExplainable AI (XAI)Interactive Data VisualizationCHI
Dark Patterns in the Opt-Out Process and Compliance with the California Consumer Privacy Act (CCPA)To protect consumer privacy, the California Consumer Privacy Act (CCPA) requires businesses to provide consumers with a straightforward way to opt out of the sale and sharing of their personal information. However, the control that businesses enjoy over the opt-out process allows them to impose hurdles on consumers aiming to opt out, including by employing dark patterns. Motivated by the enactment of the California Privacy Rights Act (CPRA), which strengthens the CCPA and explicitly forbids certain dark patterns in the opt-out process, we investigate how dark patterns are used in opt-out processes and assess their compliance with CCPA regulations. Our research on 330 CCPA-subject websites reveals that these websites employ a variety of dark patterns. Some of these patterns are explicitly prohibited under the CCPA; others seem to take advantage of legal loopholes.2025VTVan Hong Tran et al.University of Chicago, Computer SciencePrivacy by Design & User ControlDark Patterns RecognitionCHI
The Anatomy of a Plea: How Uncertainty, Visualizations & Individual Differences Shape Plea Bargain DecisionsPlea bargains are commonly used in the criminal justice system, where they can offer potential benefits to both the prosecution and the defendant. However, research has shown that defendants often engage in poor decision-making, such as accepting the plea even when the trial sentence is likely to be less severe. While previous studies have shown some evidence that uncertainty visualizations can improve decision-making, there is a lack of research on their effectiveness in domain-specific tasks like plea bargain decision-making. In this work, we conduct a series of experiments to explore whether the presence and format of uncertainty impact plea bargain decisions, taking into account time pressure and individual differences. Our findings reveal that these factors can have a significant impact on plea bargain decisions. We also show evidence that communicating uncertainty in the form of text can elicit more optimal decisions under time-pressure conditions.2025MBMelanie Bancilhon et al.US Army Research LaboratoryUncertainty VisualizationPrivacy Perception & Decision-MakingCHI
Gig2Gether: Datasharing to Empower, Unify and Demistify Gig WorkThe wide adoption of platformized work has generated remarkable advancements in the labor patterns and mobility of modern society. Underpinning such progress, gig workers are exposed to unprecedented challenges and accountabilities: lack of data transparency, social and physical isolation, as well as insufficient infrastructural safeguards. Gig2Gether presents a space designed for workers to engage in an initial experience of voluntarily contributing anecdotal and statistical data to affect policy and build solidarity across platforms by exchanging unifying and diverse experiences. Our 7-day field study with 16 active workers from three distinct platforms and work domains showed existing affordances of data-sharing: facilitating mutual support across platforms, as well as enabling financial reflection and planning. Additionally, workers envisioned future uses cases of data-sharing for collectivism (e.g., collaborative examinations of algorithmic speculations) and informing policy (e.g., around safety and pay), which motivated (latent) worker desiderata of additional capabilities and data metrics. Based on these findings, we discuss remaining challenges to address and how data-sharing tools can complement existing structures to maximize worker empowerment and policy impact.2025JHJane Hsieh et al.Carnegie Mellon University, Software and Societal Systems DepartmentAlgorithmic Transparency & AuditabilityGig Economy PlatformsCHI
TouchpadAnyWear: Textile-Integrated Tactile Sensors for Multimodal High Spatial-Resolution Touch Inputs with Motion Artifacts ToleranceThis paper presents TouchpadAnyWear, a novel family of textile-integrated force sensors capable of multi-modal touch input, encompassing micro-gesture detection, two-dimensional (2D) continuous input, and force-sensitive strokes. This thin (\textless 1.5~mm) and conformal device features high spatial resolution sensing and motion artifact tolerance through its unique capacitive sensor architecture. The sensor consists of a knitted textile compressive core, sandwiched by stretchable silver electrodes, and conductive textile shielding layers on both sides. With a high-density sensor pixel array (25/cm\textsuperscript{2}), TouchpadAnyWear can detect touch input locations and sizes with millimeter-scale spatial resolution and a wide range of force inputs (0.05~N to 20~N). The incorporation of miniature polymer domes, referred to as ``poly-islands'', onto the knitted textile locally stiffens the sensing areas, thereby reducing motion artifacts during deformation. These poly-islands also provide passive tactile feedback to users, allowing for eyes-free localization of the active sensing pixels. Design choices and sensor performance are evaluated using in-depth mechanical characterization. Demonstrations include an 8-by-8 grid sensor as a miniature high-resolution touchpad and a T-shaped sensor for thumb-to-finger micro-gesture input. User evaluations validate the effectiveness and usability of TouchpadAnyWear in daily interaction contexts, such as tapping, forceful pressing, swiping, 2D cursor control, and 2D stroke-based gestures. This paper further discusses potential applications and explorations for TouchpadAnyWear in wearable smart devices, gaming, and augmented reality devices.2024JZJunyi Zhao et al.Haptic WearablesShape-Changing Interfaces & Soft Robotic MaterialsFoot & Wrist InteractionUIST
Measuring Compliance with the California Consumer Privacy Act Over Space and TimeThe widespread sharing of consumers' personal information with third parties raises significant privacy concerns. The California Consumer Privacy Act (CCPA) mandates that online businesses offer consumers the option to opt out of the sale and sharing of personal information. Our study automatically tracking the presence of the opt-out link longitudinally across multiple states after the California Privacy Rights Act (CPRA) went into effect. We categorize websites based on whether they are subject to CCPA and investigate cases of potential non-compliance. We find a number of websites that implement the opt-out link early and across all examined states but also find a significant number of CCPA-subject websites that fail to offer any opt-out methods even when CCPA is in effect. Our findings can shed light on how websites are reacting to the CCPA and identify potential gaps in compliance and opt-out method designs that hinder consumers from exercising CCPA opt-out rights.2024VTVan Hong Tran et al.University of ChicagoPrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Contact Tracing for HealthcareWorkers in an Intensive Care Unit"The earlobe is a well-known location for wearing jewelry, but might also be promising for electronic output, such as presenting notifications. This work elaborates the pros and cons of different notification channels for the earlobe. Notifications on the earlobe can be private (only noticeable by the wearer) as well as public (noticeable in the immediate vicinity in a given social situation). A user study with 18 participants showed that the reaction times for the private channels (Poke, Vibration, Private Sound, Electrotactile) were on average less than 1 s with an error rate (missed notifications) of less than 1 %. Thermal Warm and Cold took significantly longer and Cold was least reliable (26 % error rate). The participants preferred Electrotactile and Vibration. Among the public channels the recognition time did not differ significantly between Sound (738 ms) and LED (828 ms), but Display took much longer (3175 ms). At 22 % the error rate of Display was highest. The participants generally felt comfortable wearing notification devices on their earlobe. The results show that the earlobe indeed is a suitable location for wearable technology, if properly miniaturized, which is possible for Electrotactile and LED. We present application scenarios and discuss design considerations. A small field study in a fitness center demonstrates the suitability of the earlobe notification concept in a sports context." https://doi.org/10.1145/36109242023JZJingwen Zhang et al.In-Vehicle Haptic, Audio & Multimodal FeedbackVibrotactile Feedback & Skin StimulationHaptic WearablesUbiComp
Creating Design Resources to Scaffold the Ideation of AI ConceptsAdvances in artificial intelligence have enabled unprecedented technical capabilities, yet making these advances useful in the real world remains challenging. We engaged in a Research through Design process to improve the ideation of AI products and services. We developed a design resource capturing AI capabilities based on 40 AI features commonly used across various domains. To probe its usefulness, we created a set of slides illustrating AI capabilities and asked designers to ideate AI-enabled user experiences. We also incorporated capabilities into our own design process to brainstorm concepts with domain experts and data scientists. Our research revealed that designers should focus on innovations where moderate AI performance creates value. We reflect on our process and discuss research implications for creating and assessing resources to systematically explore AI’s problem-solution space.2023NYNur Yildirim et al.Generative AI (Text, Image, Music, Video)Human-LLM CollaborationPrototyping & User TestingDIS
Why Combining Text and Visualization Could Improve Bayesian Reasoning: A Cognitive Load PerspectiveInvestigations into using visualization to improve Bayesian reasoning and advance risk communication have produced mixed results, suggesting that cognitive ability might affect how users perform with different presentation formats. Our work examines the cognitive load elicited when solving Bayesian problems using icon arrays, text, and a juxtaposition of text and icon arrays. We used a three-pronged approach to capture a nuanced picture of cognitive demand and measure differences in working memory capacity, performance under divided attention using a dual-task paradigm, and subjective ratings of self-reported effort. We found that individuals with low working memory capacity made fewer errors and experienced less subjective workload when the problem contained an icon array compared to text alone, showing that visualization improves accuracy while exerting less cognitive demand. We believe these findings can considerably impact accessible risk communication, especially for individuals with low working memory capacity.2023MBMelanie Bancilhon et al.Washington University in St LouisInteractive Data VisualizationUncertainty VisualizationVisualization Perception & CognitionCHI
Assisting Teaching Assistants with Automatic Code CorrectionsUndergraduate Teaching Assistants(TAs) in Computer Science courses are often the first and only point of contact when a student gets stuck on a programming problem. But these TAs are often relative beginners themselves, both in programming and in teaching. In this paper, we examine the impact of availability of corrected code on TAs' ability to find, fix, and address bugs in student code. We found that seeing a corrected version of the student code helps TAs debug code 29% faster, and write more accurate and complete student-facing explanations of the bugs (30% more likely to correctly address a given bug). We also observed that TAs do not generally struggle with the conceptual understanding of the underlying material. Rather, their difficulties seem more related to issues with working memory, attention, and overall high cognitive load.2022YMYana Malysheva et al.Washington University in St LouisHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationCHI
Does Interaction Improve Bayesian Reasoning with Visualization?Interaction enables users to navigate large amounts of data effectively, supports cognitive processing, and increases data representation methods. However, there have been few attempts to empirically demonstrate whether adding interaction to a static visualization improves its function beyond popular beliefs. In this paper, we address this gap. We use a classic Bayesian reasoning task as a testbed for evaluating whether allowing users to interact with a static visualization can improve their reasoning. Through two crowdsourced studies, we show that adding interaction to a static Bayesian reasoning visualization does not improve participants’ accuracy on a Bayesian reasoning task. In some cases, it can significantly detract from it. Moreover, we demonstrate that underlying visualization design modulates performance and that people with high versus low spatial ability respond differently to different interaction techniques and underlying base visualizations. Our work suggests that interaction is not as unambiguously good as we often believe; a well designed static visualization can be as, if not more, effective than an interactive one.2021AMAb Mosca et al.Tufts UniversityInteractive Data VisualizationVisualization Perception & CognitionCHI
Predicting Cognitive Load in Future Code PuzzlesCode puzzles are an increasingly popular way to introduce youth to programming. Yet our knowledge about how to maximize learning from puzzles is incomplete. We conducted a data collection study and trained a model that predicts cognitive load, the mental effort necessary to complete a task, on a future puzzle. Controlling cognitive load can lead to more effective learning. Our model suggests that it is possible to predict Cognitive Load on future problems; the model could correctly distinguish the more difficult puzzle within a pair 71%-79% of the time. Further, studying the model itself provides new insights into the sources of puzzle difficulty, the factors that contribute to Cognitive Load, and their inter-relationships. Finally, the ability to predict Cognitive Load on a future puzzle is an important step towards the creation of adaptive code puzzle systems.2019CKCaitlin Kelleher et al.Washington University in St. LouisProgramming Education & Computational ThinkingIntelligent Tutoring Systems & Learning AnalyticsCHI
ECGLens: Interactive Visual Exploration of Large Scale ECG Data for Arrhythmia DetectionThe Electrocardiogram (ECG) is commonly used to detect arrhythmias. Traditionally, a single ECG observation is used for diagnosis, making it difficult to detect irregular arrhythmias. Recent technology developments, however, have made it cost-effective to collect large amounts of raw ECG data over time. This promises to improve diagnosis accuracy, but the large data volume presents new challenges for cardiologists. This paper introduces ECGLens, an interactive system for arrhythmia detection and analysis using large-scale ECG data. Our system integrates an automatic heartbeat classification algorithm based on convolutional neural network, an outlier detection algorithm, and a set of rich interaction techniques. We also introduce A-glyph, a novel glyph designed to improve the readability and comparison of ECG signals. We report results from a comprehensive user study showing that A-glyph improves the efficiency in arrhythmia detection, and demonstrate the effectiveness of ECGLens in arrhythmia detection through two expert interviews.2018KXKe Xu et al.The Hong Kong University of Science and TechnologyInteractive Data VisualizationMedical & Scientific Data VisualizationCHI