huashen218/convxai-cia-dataset huggingface dataset:
Title: Assessing Severity of Pulmonary Obstruction from Respiration Phase-Based Wheeze-Sensing Using Mobile Sensors
Authors: family: Chatterjee
given: Soujanya; family: Rahman
given: Md Mahbubur; family: Ahmed
given: Tousif; family: Saleheen
given: Nazir; family: Nemati
given: Ebrahim; family: Nathan
given: Viswam; family: Vatanparvar
given: Korosh; family: Kuang
given: Jilong
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Obstructive pulmonary diseases cause limited airflow from the lung and severely affect patients' quality of life. Wheeze is one of the most prominent symptoms for them. High requirements imposed by traditional diagnosis methods make regular monitoring of pulmonary obstruction challenging, which hinders the opportunity of early intervention and prevention of significant exacerbation. In this work, we explore the feasibility of developing a mobile sensor-based system as a convenient means of assessing the severity of pulmonary obstruction via respiration phase-based symptomatic wheeze sensing. We conduct a 131 subjects' (91 patients and 40 healthy) study for the detection (F1: 87.96%) and characterization (F1: 79.47%) of wheeze. Subsequently, we develop novel wheeze metrics, which show a significant correlation (Pearson's correlation: -0.22, p-value: 0.024) with standard spirometry measure of pulmonary obstruction severity. This work takes a principal step towards the unobtrusive assessment of pulmonary condition from mobile sensor interactions.
Title: Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI
Authors: family: Madaio
given: Michael A.; family: Stark
given: Luke; family: Wortman Vaughan
given: Jennifer; family: Wallach
given: Hanna
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Many organizations have published principles intended to guide the ethical development and deployment of AI systems; however, their abstract nature makes them difficult to operationalize. Some organizations have therefore produced AI ethics checklists, as well as checklists for more specific concepts, such as fairness, as applied to AI systems. But unless checklists are grounded in practitioners' needs, they may be misused. To understand the role of checklists in AI ethics, we conducted an iterative co-design process with 48 practitioners, focusing on fairness. We co-designed an AI fairness checklist and identified desiderata and concerns for AI fairness checklists in general. We found that AI fairness checklists could provide organizational infrastructure for formalizing ad-hoc processes and empowering individual advocates. We highlight aspects of organizational culture that may impact the efficacy of AI fairness checklists, and suggest future design directions.
Title: Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games
Authors: family: Frommel
given: Julian; family: Sagl
given: Valentin; family: Depping
given: Ansgar E.; family: Johanson
given: Colby; family: Miller
given: Matthew K.; family: Mandryk
given: Regan L.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Online social interactions in multiplayer games can be supportive and positive or toxic and harmful; however, few methods can easily assess interpersonal interaction quality in games. We use behavioural traces to predict affiliation between dyadic strangers, facilitated through their social interactions in an online gaming setting. We collected audio, video, in-game, and self-report data from 23 dyads, extracted 75 features, trained Random Forest and Support Vector Machine models, and evaluated their performance predicting binary (high/low) as well as continuous affiliation toward a partner. The models can predict both binary and continuous affiliation with up to 79.1% accuracy (F1) and 20.1% explained variance (R2) on unseen data, with features based on verbal communication demonstrating the highest potential. Our findings can inform the design of multiplayer games and game communities, and guide the development of systems for matchmaking and mitigating toxic behaviour in online games.
Title: Silva: Interactively Assessing Machine Learning Fairness Using Causality
Authors: family: Yan
given: Jing Nathan; family: Gu
given: Ziwei; family: Lin
given: Hubert; family: Rzeszotarski
given: Jeffrey M.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Machine learning models risk encoding unfairness on the part of their developers or data sources. However, assessing fairness is challenging as analysts might misidentify sources of bias, fail to notice them, or misapply metrics. In this paper we introduce Silva, a system for exploring potential sources of unfairness in datasets or machine learning models interactively. Silva directs user attention to relationships between attributes through a global causal view, provides interactive recommendations, presents intermediate results, and visualizes metrics. We describe the implementation of Silva, identify salient design and technical challenges, and provide an evaluation of the tool in comparison to an existing fairness optimization tool.
Title: Transparency of CHI Research Artifacts: Results of a Self-Reported Survey
Authors: family: Wacharamanotham
given: Chat; family: Eisenring
given: Lukas; family: Haroz
given: Steve; family: Echtler
given: Florian
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Several fields of science are experiencing a "replication crisis" that has negatively impacted their credibility. Assessing the validity of a contribution via replicability of its experimental evidence and reproducibility of its analyses requires access to relevant study materials, data, and code. Failing to share them limits the ability to scrutinize or build-upon the research, ultimately hindering scientific progress.Understanding how the diverse research artifacts in HCI impact sharing can help produce informed recommendations for individual researchers and policy-makers in HCI. Therefore, we surveyed authors of CHI 2018-2019 papers, asking if they share their papers' research materials and data, how they share them, and why they do not. The results (34% response rate) show that sharing is uncommon, partly due to misunderstandings about the purpose of sharing and reliable hosting. We conclude with recommendations for fostering open research practices.This paper and all data and materials are freely available at https://osf.io/3bu6t.
Title: Quantification of Users' Visual Attention During Everyday Mobile Device Interactions
Authors: family: Bâce
given: Mihai; family: Staal
given: Sander; family: Bulling
given: Andreas
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: We present the first real-world dataset and quantitative evaluation of visual attention of mobile device users in-situ, i.e. while using their devices during everyday routine. Understanding user attention is a core research challenge in mobile HCI but previous approaches relied on usage logs or self-reports that are only proxies and consequently do neither reflect attention completely nor accurately. Our evaluations are based on Everyday Mobile Visual Attention (EMVA) – a new 32-participant dataset containing around 472 hours of video snippets recorded over more than two weeks in real life using the front-facing camera as well as associated usage logs, interaction events, and sensor data. Using an eye contact detection method, we are first to quantify the highly dynamic nature of everyday visual attention across users, mobile applications, and usage contexts. We discuss key insights from our analyses that highlight the potential and inform the design of future mobile attentive user interfaces.
Title: Investigating User Expectations on the Roles of Family-shared AI Speakers
Authors: family: Park
given: Sunjeong; family: Lim
given: Youn-kyung
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: AI assistants that use a voice user interface (VUI), such as AI speakers, have become popular in family homes. However, it is still unclear what roles the AI speaker can support within the family unit. We investigated the roles of an AI speaker as a family-shared technology. By conducting a one-week participatory user study, we discovered that family members' co-ownership toward the AI speaker was the key in the development of its family-oriented roles. Our findings showed seven domains of user expectations on these roles, and we realized that all the expectations can be represented as family cohesion. In addition, privacy awareness was emphasized regarding personal supports. Finally, we discuss a new perspective for AI speaker design and offer two suggestions: 1) leveraging human-likeness to develop its potential roles of supporting the unit of a family and 2) interpreting the home context to seamlessly connect family and personal supporting roles.
Title: C-Space: An Interactive Prototyping Platform for Collaborative Spatial Design Exploration
Authors: family: Son
given: Kihoon; family: Chun
given: Hwiwon; family: Park
given: Sojin; family: Hyun
given: Kyung Hoon
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: C-Space is an interactive prototyping platform for collaborative spatial design exploration. Spatial design projects often begin with conceptualization that includes abstract diagramming, zoning, and massing to provide a foundation for making design decisions. Specifically, abstract diagrams guide designers to explore alternative designs without thinking prematurely about the details. However, complications arise when communicating ambiguous and incomplete designs to collaborators. To overcome this drawback, designers devote considerable amounts of time and resources into searching for design references and creating rough prototypes to explicate their design concepts better. Therefore, this study proposes C-Space, a novel design support system that integrates the abstract diagram with design reference retrieval and prototyping through a tangible user interface and augmented reality. Through a user study with 12 spatial designers, we verify that C-Space promotes rapid and robust spatial design exploration, inducing collaborative discussions and motivating users to interact with designs.
Title: Metaprobes, Metaphysical Workshops and Sketchy Philosophy
Authors: family: Encinas
given: Enrique; family: Durrant
given: Abigail C.; family: Mitchell
given: Robb; family: Blythe
given: Mark
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: The intersection of philosophy and HCI is a longstanding site of interest for the field that has been attracting special attention in recent years. In this paper, we present metaphysical probes (Metaprobes) as a tool for design-led philosophical inquiry. A Metaprobe is a design artifact used to study a metaphysical idea without concealing the philosophical tools mobilized by the designers or the designerly knowledge attained after deployment. We introduce the concept of a Metaphysical Workshop. This is the set of sketchy philosophical notions that a designer mobilizes in order to research a philosophical idea through design. We then present a case study that comprises: the philosophical issue under examination, the Metaprobes designed to study it, the metaphysical workshop used and the designerly insight produced. We conclude with a discussion of the potentials and weaknesses of Metaprobes in relation to other critical and speculative research-through-design practices. We aim to provide one way to make philosophies already present in design more explicit and make other philosophical concepts relevant to HCI more accessible and workable for designers.
Title: How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results
Authors: family: Hofman
given: Jake M.; family: Goldstein
given: Daniel G.; family: Hullman
given: Jessica
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: When presenting visualizations of experimental results, scientists often choose to display either inferential uncertainty (e.g., uncertainty in the estimate of a population mean) or outcome uncertainty (e.g., variation of outcomes around that mean) about their estimates. How does this choice impact readers' beliefs about the size of treatment effects? We investigate this question in two experiments comparing 95% confidence intervals (means and standard errors) to 95% prediction intervals (means and standard deviations). The first experiment finds that participants are willing to pay more for and overestimate the effect of a treatment when shown confidence intervals relative to prediction intervals. The second experiment evaluates how alternative visualizations compare to standard visualizations for different effect sizes. We find that axis rescaling reduces error, but not as well as prediction intervals or animated hypothetical outcome plots (HOPs), and that depicting inferential uncertainty causes participants to underestimate variability in individual outcomes.
Title: Integrated Development Environment with Interactive Scatter Plot for Examining Statistical Modeling
Authors: family: Asai
given: Kentaro; family: Fukusato
given: Tsukasa; family: Igarashi
given: Takeo
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: The development of a statistical modeling program requires example data to observe and verify the behavior of the program. Such example data are either taken from an existing dataset or synthesized using commands. Programmers may want to directly design an arbitrary dataset or modify it interactively, but it is difficult to do so in current development environments. We therefore propose combining a code editor with an interactive scatter plot editor to efficiently understand the behavior of statistical modeling algorithms. The user interactively creates and modifies the dataset on the scatter plot editor, while the system continuously executes the code in the editor, taking the data as input, and shows the result in the editor. This paper presents the design rationale behind the system and introduces several usage scenarios.
Title: Many Faced Hate: A Cross Platform Study of Content Framing and Information Sharing by Online Hate Groups
Authors: family: Phadke
given: Shruti; family: Mitra
given: Tanushree
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Hate groups are increasingly using multiple social media platforms to promote extremist ideologies. Yet we know little about their communication practices across platforms. How do hate groups (or "in-groups"), frame their hateful agenda against the targeted group or the "out-group?" How do they share information? Utilizing "framing" theory from social movement research and analyzing domains in the shared links, we juxtapose the Facebook and Twitter communication of 72 Southern Poverty Law Center (SPLC) designated hate groups spanning five hate ideologies. Our findings show that hate groups use Twitter for educating the audience about problems with the out-group, maintaining positive self-image by emphasizing in-group's high social status, and for demanding policy changes to negatively affect the out-group. On Facebook, they use fear appeals, call for active participation in group events (membership requests), all while portraying themselves as being oppressed by the out-group and failed by the system. Our study unravels the ecosystem of cross-platform communication by hate groups, suggesting that they use Facebook for group radicalization and recruitment, while Twitter for reaching a diverse follower base.
Title: How Mandatory Second Factor Affects the Authentication User Experience
Authors: family: Abbott
given: Jacob; family: Patil
given: Sameer
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Recent years have seen growing organizational adoption of two-factor authentication as organizations seek to limit the damage caused by password breaches. However, research on the user experience of two-factor authentication in a real-world setting is relatively scant. To fill this gap, we conducted multiple waves of an online survey of users at a large public university during its multi-phase rollout of mandatory two-factor authentication for faculty, staff, and students. In addition, we examined multiple months of logs of all authentication events at the university. We found no significant changes in user experience and acceptance of two-factor authentication when it was mandatory for select systems that dealt with sensitive information. However, these factors degraded when users were forced to use two-factor authentication for logging into every single university resource. Our findings can serve as important guidance for the implementation of two-factor authentication in organizations in a way that can help achieve a balance between security and user experience.
Title: Supporting the Supporters of Unaccompanied Migrant Youth: Designing for Social-ecological Resilience
Authors: family: Tachtler
given: Franziska; family: Michel
given: Toni; family: Slovák
given: Petr; family: Fitzpatrick
given: Geraldine
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Unaccompanied migrant youth, fleeing to a new country without their parents, are exposed to mental health risks. Resilience interventions mitigate such risks, but access can be hindered by systemic and personal barriers. While much work has recently addressed designing technology to promote mental health, none has focused on the needs of these populations. This paper presents the results of interviews with 18 professional/ volunteer support workers and 5 unaccompanied migrant youths, followed by three design workshops. The results point to the diverse systems that can facilitate youths' resilience development. The relationship between the youth and volunteers acting as mentors is particularly important for increasing resilience but comes with challenges. This suggests the relevance of a social-ecological model of resilience with a focus on designing technology to support the mentors in order to help them better support the youth. We conclude by mapping out the design space for mentor support.
Title: Dimensions of UX Practice that Shape Ethical Awareness
Authors: family: Chivukula
given: Shruthi Sai; family: Watkins
given: Chris Rhys; family: Manocha
given: Rhea; family: Chen
given: Jingle; family: Gray
given: Colin M.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: HCI researchers are increasingly interested in describing the complexity of design practice, including ethical, organizational, and societal concerns. Recent studies have identified individual practitioners as key actors in driving the design process and culture within their respective organizations, and we build upon these efforts to reveal practitioner concerns regarding ethics on their own terms. In this paper, we report on the results of an interview study with eleven UX practitioners, capturing their experiences that highlight dimensions of design practice that impact ethical awareness and action. Using a bottom-up thematic analysis, we identified five dimensions of design complexity that influence ethical outcomes and span individual, collaborative, and methodological framing of UX activity. Based on these findings, we propose a set of implications for the creation of ethically-centered design methods that resonate with this complexity and inform the education of future UX practitioners.
Title: Storytelling to Sensemaking: A Systematic Framework for Designing Auditory Description Display for Interactives
Authors: family: Smith
given: Taliesin L.; family: Moore
given: Emily B.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Auditory description display is verbalized text typically used to describe live, recorded, or graphical displays to support access for people who are blind or visually impaired. Significant prior research has resulted in guidelines for auditory description for non-interactive or minimally interactive contexts. A lack of auditory description for complex interactive environments remains a tremendous barrier to access for people with visual impairments. In this work, we present a systematic design framework for designing auditory description within complex interactive environments. We illustrate how modular descriptions aligned with this framework can result in an interactive storytelling experience constructed through user interactions. This framework has been used in a set of published and widely used interactive science simulations, and in its generalized form could be applied to a variety of contexts.
Title: Empathy Is All You Need: How a Conversational Agent Should Respond to Verbal Abuse
Authors: family: Chin
given: Hyojin; family: Molefi
given: Lebogang Wame; family: Yi
given: Mun Yong
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: With the popularity of AI-infused systems, conversational agents (CAs) are becoming essential in diverse areas, offering new functionality and convenience, but simultaneously, suffering misuse and verbal abuse. We examine whether conversational agents' response styles under varying abuse types influence those emotions found to mitigate peoples' aggressive behaviors, involving three verbal abuse types (Insult, Threat, Swearing) and three response styles (Avoidance, Empathy, Counterattacking). Ninety-eight participants were assigned to one of the abuse type conditions, interacted with the three spoken (voice-based) CAs in turn, and reported their feelings about guiltiness, anger, and shame after each session. The results show that the agent's response style has a significant effect on user emotions. Participants were less angry and more guilty with the empathy agent than the other two agents. Furthermore, we investigated the current status of commercial CAs' responses to verbal abuse. Our study findings have direct implications for the design of conversational agents.
Title: Brooke Leave Home: Designing a Personalized Film to Support Public Engagement with Open Data
Authors: family: Concannon
given: Shauna; family: Rajan
given: Natasha; family: Shah
given: Parthiv; family: Smith
given: Davy; family: Ursu
given: Marian; family: Hook
given: Jonathan
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Brooke Leave Home is a personalized film designed to engage a non-expert audience with open data about the support young adults receive when leaving the care system in England. The film draws upon a range of video-based data storytelling techniques to present each viewer with a personalized perspective on the topic based on data from their own local area. We present the film's design and describe how its storytelling techniques were developed to support viewers in understanding, and fostering empathic connections with, the data sources featured and the implications they have for care leavers. We also present a study with 47 viewers, which explores how these techniques were experienced and how effective they were in aiding engagement with the data included and its meaning.