Decline Now: A Combinatorial Model for Algorithmic Collective ActionDrivers on food delivery platforms often run a loss on low-paying orders. In response, workers on DoorDash started a campaign, \#DeclineNow, to purposefully decline orders below a certain pay threshold. For each declined order, the platform returns the request to other available drivers with slightly increased pay. While contributing to overall pay increase the implementation of the strategy comes with the risk of missing out on orders for each individual driver. In this work, we propose a first combinatorial model to study the strategic interaction between workers and the platform. Within our model, we formalize key quantities such as the collective benefit of the strategy, the benefit of freeriding, as well as the benefit of participation. We extend our theoretical results with simulations. Our key insights show that the collective benefit of the strategy is always positive, while the benefit of participation is positive only for small degrees of labor oversupply. Beyond this point, the utility of participants decreases faster with increasing degree of oversupply, compared to the return of freeriding. Our work highlights the significance of labor supply levels for the effectiveness of collective action on gig platforms. We discuss organizing in shifts as a means to reduce oversupply and empower collectives.2025DSDorothee Sigg et al.Max Planck Institute for Intelligent Systems; Tübingen AI CenterOnline Harassment & Counter-ToolsGig Economy PlatformsCHI
Please Understand My Disability: An Analysis of YouTubers’ Discourse on Disability ChallengesVideo-sharing platforms offer a unique avenue for people with disabilities (PWDs) to highlight their experiences, including the challenges and accessibility barriers they face. While creators with disabilities effectively use these platforms to share their life struggles and advocate for societal changes, the scope of research exploring the nature of the discourse activities related to disability challenges remains limited. Our study addresses this gap by conducting a comprehensive qualitative content analysis of 468 videos posted by YouTubers with a range of disabilities, including vision, speech, mobility, hearing, and cognitive and neural impairments. Our findings reveal a predominant discussion on stigma and lack of support. YouTube is also used to share difficulties related to communication and systemic problems. Creators with disabilities also share experiences with technologies and public and private environments, through which they discuss accessibility issues and solutions. Building on our analysis, we propose future research directions aimed at enhancing the experience and support for disability communities on video-sharing platforms.2024SNShuo Niu et al.Session 2c: Blind Users and Collaborative SensingCSCW
The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture TechnologyMotion capture systems, used across various domains, make body representations concrete through technical processes. We argue that the measurement of bodies and the validation of measurements for motion capture systems can be understood as social practices. By analyzing the findings of a systematic literature review (N=278) through the lens of social practice theory, we show how these practices, and their varying attention to errors, become ingrained in motion capture design and innovation over time. Moreover, we show how contemporary motion capture systems perpetuate assumptions about human bodies and their movements. We suggest that social practices of measurement and validation are ubiquitous in the development of data- and sensor-driven systems more broadly, and provide this work as a basis for investigating hidden design assumptions and their potential negative consequences in human-computer interaction.2024EHEmma Harvey et al.Cornell UniversityHuman Pose & Activity RecognitionComputational Methods in HCICHI
Smooth as - The Effects of Frame Rate Variation on Game Player Quality of ExperienceFor gamers, high frame rates are important for a smooth visual display and good quality of experience (QoE). However, high frame rates alone are not enough as variations in the frame display times can degrade QoE even as the average frame rate remains high. While the impact of steady frame rates on player QoE is fairly well-studied, the effects of frame rate variation is not. This paper presents a 33-person user study that evaluates the impact of frame rate variation on users playing three different computer games. Analysis of the results shows average frame rate alone is a poor predictor of QoE, and frame rate variation has a significant impact on player QoE. While the standard deviation of frame times is promising as a general predictor for QoE, frame time standard deviation may not be accurate for all individual games. However, 95% frame rate floor -– the bottom 5% of frame rates the player experiences –- appears to be an effective predictor of both QoE overall and for the individual games tested.2023SLShengmei Liu et al.Worcester Polytechnic InstituteGame UX & Player BehaviorGamification DesignCHI
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI InteractionDespite the proliferation of explainable AI (XAI) methods, little is understood about end-users' explainability needs and behaviors around XAI explanations. To address this gap and contribute to understanding how explainability can support human-AI interaction, we conducted a mixed-methods study with 20 end-users of a real-world AI application, the Merlin bird identification app, and inquired about their XAI needs, uses, and perceptions. We found that participants desire practically useful information that can improve their collaboration with the AI, more so than technical system details. Relatedly, participants intended to use XAI explanations for various purposes beyond understanding the AI's outputs: calibrating trust, improving their task skills, changing their behavior to supply better inputs to the AI, and giving constructive feedback to developers. Finally, among existing XAI approaches, participants preferred part-based explanations that resemble human reasoning and explanations. We discuss the implications of our findings and provide recommendations for future XAI design.2023SKSunnie S. Y. Kim et al.Princeton UniversityExplainable AI (XAI)AI-Assisted Decision-Making & AutomationCHI
FAR: End-to-End Vibrotactile Distributed System Designed to Facilitate Affect Regulation in Children Diagnosed with Autism Spectrum Disorder Through Slow BreathingTo address difficulties with affect dysregulation in youth diagnosed with autism spectrum disorder (ASD), we designed and developed an end-to-end vibrotactile breathing pacer system and evaluated its usability. In this paper we describe the system architecture and the features we deployed for this system based on expert advice and reviews. Through piloting this system with one child diagnosed with ASD, we learned that our system was used in ways we did and did not anticipate. For example, the paced-breathing personalization procedure did not meet the attention span of the pilot participant but two instead of one pacer devices encouraged caregiver’s involvement. This paper details our learnings and concludes with a list of system design guidelines at the system architecture level. To the best of our knowledge, this is the first fully functional vibrotactile system designed for ASD children that withstood usability testing in vitro for two weeks.2022PMPardis Miri et al.Stanford University, Stanford UniversityVibrotactile Feedback & Skin StimulationCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)CHI
Lower is Better? The Effects of Local Latencies on Competitive First-Person Shooter Game PlayersVideo game play is among the most popular forms of entertainment in the world and eSports is a multi-billion dollar industry. Esports gamers, and competitive gamers more broadly, want fast game systems to maximize their chances of winning. In general, the faster the game system, the lower the latency between a player's action and the intended outcome. But how much small reductions in local latencies benefit competitive players is not known. This paper presents results from a 43-person user study that evaluates the impact of system latencies for high-end gaming systems (below 125 ms) on experienced Counter-strike: Global Offensive (CS:GO) players. Analysis of the results show pronounced benefits to CS:GO player performance (accuracy and score) for even small reductions in latency, with subjective opinions on Quality of Experience following suit.2021SLShengmei Liu et al.Worcester Polytechnic InstituteGame UX & Player BehaviorMultiplayer & Social GamesCHI
Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry StrategiesIntelligent conversational agents, or chatbots, can take on various identities and are increasingly engaging in more human-centered conversations with persuasive goals. However, little is known about how identities and inquiry strategies influence the conversation's effectiveness. We conducted an online study involving 790 participants to be persuaded by a chatbot for charity donation. We designed a two by four factorial experiment (two chatbot identities and four inquiry strategies) where participants were randomly assigned to different conditions. Findings showed that the perceived identity of the chatbot had significant effects on the persuasion outcome (i.e., donation) and interpersonal perceptions (i.e., competence, confidence, warmth, and sincerity). Further, we identified interaction effects among perceived identities and inquiry strategies. We discuss the findings for theoretical and practical implications for developing ethical and effective persuasive chatbots. Our published data, codes, and analyses serve as the first step towards building competent ethical persuasive chatbots.2020WSWeiyan Shi et al.University of California, DavisConversational ChatbotsAgent Personality & AnthropomorphismCHI
Optimizing User Interface Layouts via Gradient DescentAutomating parts of the user interface (UI) design process has been a longstanding challenge. We present an automated technique for optimizing the layouts of mobile UIs. Our method uses gradient descent on a neural network model of task performance with respect to the model's inputs to make layout modifications that result in improved predicted error rates and task completion times. We start by extending prior work on neural network based performance prediction to 2-dimensional mobile UIs with an expanded interaction space. We then apply our method to two UIs, including one that the model had not been trained on, to discover layout alternatives with significantly improved predicted performance. Finally, we confirm these predictions experimentally, showing improvements up to 9.2 percent in the optimized layouts. This demonstrates the algorithm's efficacy in improving the task performance of a layout, and its ability to generalize and improve layouts of new interfaces.2020PDPeitong Duan et al.Intel AIPrototyping & User TestingComputational Methods in HCICHI
Evaluating a Personalizable, Inconspicuous Vibrotactile(PIV) Breathing Pacer for In-the-Moment Affect RegulationGiven the prevalence and adverse impact of anxiety, there is considerable interest in using technology to regulate anxiety. Evaluating the efficacy of such technology in terms of both the average effect (the intervention efficacy) and the heterogeneous effect (for whom and in what context the intervention was effective) is of paramount importance. In this paper, we demonstrate the efficacy of PIV, a personalized breathing pacer, in reducing anxiety in the presence of a cognitive stressor. We also quantify the relation between our specific stressor and PIV-user engagement. To our knowledge, this is the first mixed-design study of a vibrotactile affect regulation technology which accounts for a specific stressor and for individual differences in relation to the technology's efficacy. Guidelines in this paper can be applied for designing and evaluating other affect regulation technologies.2020PMPardis Miri et al.Stanford UniversityVibrotactile Feedback & Skin StimulationSleep & Stress MonitoringCHI
Protection, Productivity and Pleasure in the Smart Home: Emerging Expectations and Gendered Insights from Australian Early AdoptersInterest and uptake of smart home technologies has been lower than anticipated, particularly among women. Reporting on an academic-industry partnership, we present findings from an ethnographic study with 31 Australian smart home early adopters. The paper analyses these households' experiences in relation to three concepts central to Intel's ambient computing vision for the home: protection, productivity and pleasure, or 'the 3Ps'. We find that protection is a form of caregiving; productivity provides 'small conveniences', energy savings and multi-tasking possibilities; and pleasure is derived from ambient and aesthetic features, and the joy of 'playing around' with tech. Our analysis identifies three design challenges and opportunities for the smart home: internal threats to household protection; feminine desires for the smart home; and increased 'digital housekeeping'. We conclude by suggesting how HCI designers can and should respond to these gendered challenges.2019YSYolande Strengers et al.Monash UniversitySmart Home Interaction DesignInclusive DesignCHI
Investigating the Impact of a Real-time, Multimodal Student Engagement Analytics Technology in Authentic ClassroomsWe developed a real-time, multimodal Student Engagement Analytics Technology so that teachers can provide just-in-time personalized support to students who risk disengagement. To investigate the impact of the technology, we ran an exploratory semester-long study with a teacher in two classrooms. We used a multi-method approach consisting of a quasi-experimental design to evaluate the impact of the technology and a case study design to understand the environmental and social factors surrounding the classroom setting. The results show that the technology had a significant impact on the teacher's classroom practices (i.e., increased scaffolding to the students) and student engagement (i.e., less boredom). These results suggest that the technology has the potential to support teachers' role of being a coach in technology-mediated learning environments.2019SASinem Aslan et al.Intel CorporationProgramming Education & Computational ThinkingIntelligent Tutoring Systems & Learning AnalyticsCollaborative Learning & Peer TeachingCHI
A Bot is Not a Polyglot: Designing Personalities for Multi-Lingual Conversational AgentsConversational agents are becoming more common, influenced by the success of Siri and Alexa. As such, new methods and associated challenges of designing for conversational systems are emerging. One factor, unique to conversational agents, that we need to account for as designers, is personality. People attribute personalities to conversational agents, strongly influenced by social expectations, whether or not a particular personality was designed deliberately. In the case of multi-lingual agents, this creates additional challenges: direct translations don’t accommodate cultural variation. We discuss the design process and lessons learned from Radar Pace, a conversational coach that launched with support for five languages. We highlight successes and failures based on user study results and propose changes to the design process to avoid pitfalls for future agents.2018ADAndreea Danielescu et al.Intel Corp.Intelligent Voice Assistants (Alexa, Siri, etc.)Multilingual & Cross-Cultural Voice InteractionAgent Personality & AnthropomorphismCHI
Seismo: Blood Pressure Monitoring using Built-in Smartphone Accelerometer and CameraAlthough cost-effective at-home blood pressure monitors are available, a complementary mobile solution can ease the burden of measuring BP at critical points throughout the day. In this work, we developed and evaluated a smartphone-based BP monitoring application called textit{Seismo}. The technique relies on measuring the time between the opening of the aortic valve and the pulse later reaching a periphery arterial site. It uses the smartphone's accelerometer to measure the vibration caused by the heart valve movements and the smartphone's camera to measure the pulse at the fingertip. The system was evaluated in a nine participant longitudinal BP perturbation study. Each participant participated in four sessions that involved stationary biking at multiple intensities. The Pearson correlation coefficient of the blood pressure estimation across participants is 0.20-0.77 ($mu$=0.55, $sigma$=0.19), with an RMSE of 3.3-9.2 mmHg ($mu$=5.2, $sigma$=2.0).2018EWEdward Jay Wang et al.University of WashingtonSmartwatches & Fitness BandsBiosensors & Physiological MonitoringCHI
The Influence of Friends and Experts on Privacy Decision Making in IoT ScenariosAs increasingly many Internet-of-Things (IoT) devices collect personal data, users face more privacy decisions. Personal privacy assistants can provide social cues and help users make informed decisions by presenting information about how others have decided in similar cases. To better understand which social cues are relevant and whose recommendations users are more likely to follow, we presented 1000 online participants with nine IoT data collection scenarios. Some participants were told the percentage of experts or friends who allowed data collection in each scenario, while other participants were provided no social cue. At the conclusion of each scenario, participants were asked whether they would allow the described data collection. Our results indicate that when friends denied data collection, our participants were more influenced than when friends allowed data collection. On the other hand, participants were more influenced by experts when they allowed data collection.In addition, we observed that influence could get stronger or wear off over a repeated sequence of scenarios. For example, when experts and friends repeatedly allowed scenarios with clear risk or denied scenarios with clear benefits, participants were less likely to be influenced by them in subsequent scenarios.2018PNPardis Emami-Naeini et al.Privacy in Homes and GroupsCSCW