huashen218/convxai-cia-dataset huggingface dataset:
Title: “Good Enough!”: Flexible Goal Achievement with Margin-based Outcome Evaluation
Authors: family: Jung
given: Gyuwon; family: Oh
given: Jio; family: Jung
given: Youjin; family: Sun
given: Juho; family: Kong
given: Ha-Kyung; family: Lee
given: Uichin
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Service Design, User Experience Design, Behavior Change, Interview

Abstract: Traditional goal setting simply assumes a binary outcome for goal evaluation. This binary judgment does not consider a user’s effort, which may demotivate the user. This work explores the possibility of mitigating this negative impact with a slight modification on the goal evaluation criterion, by introducing a ‘margin’ that is widely used for quality control in the manufacturing fields. A margin represents a range near the goal where the user’s outcome will be regarded as ‘good enough’ even if the user fails to reach it. We explore users’ perceptions and behaviors through a large-scale survey study and a small-scale field experiment using a coaching system to promote physical activity. Our results provide positive evidence on the margin, such as lowering the burden of goal achievement and increasing motivation to make attempts. We discuss practical design implications on margin-enabled goal setting and evaluation for behavioral change support systems.
Title: “That courage to encourage”: Participation and Aspirations in Chat-based Peer Support for Youth Living with HIV
Authors: family: Karusala
given: Naveena; family: Seeh
given: David Odhiambo; family: Mugo
given: Cyrus; family: Guthrie
given: Brandon; family: Moreno
given: Megan A; family: John-Stewart
given: Grace; family: Inwani
given: Irene; family: Anderson
given: Richard; family: Ronen
given: Keshet
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: peer support, Kenya, health, chat apps, aspirations, WhatsApp

Abstract: We present a qualitative study of a six-month pilot of WhatsApp-based facilitated peer support groups, serving youth living with human immunodeficiency virus (HIV) in an informal settlement in Nairobi, Kenya. Popular chat apps are increasingly being leveraged to make a combination of patient-provider communication and peer support more accessible beyond formal healthcare settings. However, how these interventions are experienced in Global South contexts with phone sharing and intermittent data access is understudied. The context of stigmatized illnesses like HIV further complicates privacy concerns. We draw on chat records and interviews with youth and the facilitator to describe their experience of the intervention. We find that despite tensions in group dynamics, intermittent participation, and contingencies around privacy, youth were motivated by newfound aspirations and community to manage their health. We use our findings to discuss implications for the design of chat-based peer interventions, negotiation of privacy in mobile health applications, and the role of aspirations in health interventions.
Title: Transgender People’s Technology Needs to Support Health and Transition
Authors: family: Chuanromanee
given: Tya; family: Metoyer
given: Ronald
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: information management, transgender health, wellness, inclusive design, gender dysphoria

Abstract: Health and well-being are integral parts of the human experience, and yet due to numerous factors are inaccessible for many communities. The transgender community is no exception and faces increased risk for both physical and mental illness. This population faces many unique challenges before, during, and after transition. To gain a deeper understanding of the trans community’s health and well-being needs, we conducted twenty-one interviews with transgender individuals to determine how they navigated their identities and transitions. From our interviews, we examine and highlight the unique needs of the trans population with respect to health, well-being, identity, and transition. We discuss how designers can better understand and accommodate the diversity of this community, give suggestions for the design of technologies for trans health and well-being, and contribute open areas of research.
Title: “It’s About Missing Much More Than the People”: How Students use Digital Technologies to Alleviate Homesickness
Authors: family: Kelly
given: Ryan M.; family: Cheng
given: Yueyang; family: McKay
given: Dana; family: Wadley
given: Greg; family: Buchanan
given: George
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Homesickness, Students;, Home, Migrants

Abstract: Homesickness, which refers to feelings of distress caused by separation from home, is prevalent among university-aged students. Chronic homesickness can exacerbate mood problems, erode academic performance and lead to dropout from school. The present research examines how students use digital technologies to resolve the experience of missing home. Qualitative interviews and diaries with 50 students at major Australian universities revealed that technologies play a significant role in alleviating homesickness. Specifically, students use technologies to acquire social contact, find help and support, build co-presence to recreate their home, connect with culture, experience distant places, and regulate emotions. However, the use of technology sometimes led to increased emotional labour, frequent exposure to homesickness triggers, and heightened perceptions of distance. We conclude by discussing possible design implications for new technologies that allow students to alleviate homesickness by experiencing their home from afar.
Title: Self-E: Smartphone-Supported Guidance for Customizable Self-Experimentation
Authors: family: Daskalova
given: Nediyana; family: Kyi
given: Eindra; family: Ouyang
given: Kevin; family: Borem
given: Arthur; family: Chen
given: Sally; family: Park
given: Sung Hyun; family: Nugent
given: Nicole; family: Huang
given: Jeff
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: self-tracking, self-experiments, personal informatics

Abstract: The ubiquity of self-tracking devices and smartphone apps has empowered people to collect data about themselves and try to self-improve. However, people with little to no personal analytics experience may not be able to analyze data or run experiments on their own (self-experiments). To lower the barrier to intervention-based self-experimentation, we developed an app called Self-E, which guides users through the experiment. We conducted a 2-week diary study with 16 participants from the local population and a second study with a more advanced group of users to investigate how they perceive and carry out self-experiments with the help of Self-E, and what challenges they face. We find that users are influenced by their preconceived notions of how healthy a given behavior is, making it difficult to follow Self-E’s directions and trusting its results. We present suggestions to overcome this challenge, such as by incorporating empathy and scaffolding in the system.
Title: TiltChair: Manipulative Posture Guidance by Actively Inclining the Seat of an Office Chair
Authors: family: Fujita
given: Kazuyuki; family: Suzuki
given: Aoi; family: Takashima
given: Kazuki; family: Ikematsu
given: Kaori; family: Kitamura
given: Yoshifumi
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: well-being, adaptive workplaces, actuated furniture, posture change

Abstract: We propose TiltChair, an actuated office chair that physically manipulates the user’s posture by actively inclining the chair’s seat to address problems associated with prolonged sitting. The system controls the inclination angle and motion speed with the aim of achieving manipulative but unobtrusive posture guidance. To demonstrate its potential, we first built a prototype of TiltChair with a seat that could be tilted by pneumatic control. We then investigated the effects of the seat’s inclination angle and motions on task performance and overall sitting experience through two experiments. The results show that the inclination angle mainly affects the difficulty of maintaining one’s posture, while the motion speed affected the conspicuousness and subjective acceptability of the motion. However, these seating conditions did not affect objective task performance. Based on these results, we propose a design space for facilitating effective seat-inclination behavior using the three dimensions of angle, speed, and continuity. Furthermore, we discuss promising applications.
Title: Not Now, Ask Later: Users Weaken Their Behavior Change Regimen Over Time, But Expect To Re-Strengthen It Imminently
Authors: family: Kovacs
given: Geza; family: Wu
given: Zhengxuan; family: Bernstein
given: Michael S.
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: distractions and interruptions, behavior change

Abstract: How effectively do we adhere to nudges and interventions that help us control our online browsing habits? If we have a temporary lapse and disable the behavior change system, do we later resume our adherence, or has the dam broken? In this paper, we investigate these questions through log analyses of 8,000+ users on HabitLab, a behavior change platform that helps users reduce their time online. We find that, while users typically begin with high-challenge interventions, over time they allow themselves to slip into easier and easier interventions. Despite this, many still expect to return to the harder interventions imminently: they repeatedly choose to be asked to change difficulty again on the next visit, declining to have the system save their preference for easy interventions.
Title: Sticky Goals: Understanding Goal Commitments for Behavioral Changes in the Wild
Authors: family: Lee
given: Hyunsoo; family: Kim
given: Auk; family: Hong
given: Hwajung; family: Lee
given: Uichin
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Commitment Device, Online Behavior Change Support Systems, Goal-Setting, Usage Data Analysis

Abstract: A commitment device, an attempt to bind oneself for a successful goal achievement, has been used as an effective strategy to promote behavior change. However, little is known about how commitment devices are used in the wild, and what aspects of commitment devices are related to goal achievements. In this paper, we explore a large-scale dataset from stickK, an online behavior change support system that provides both financial and social commitments. We characterize the patterns of behavior change goals (e.g., topics and commitment setting) and then perform a series of multilevel regression analyses on goal achievements. Our results reveal that successful goal achievements are largely dependent on the configuration of financial and social commitment devices, and a mixed commitment setting is considered beneficial. We discuss how our findings could inform the design of effective commitment devices, and how large-scale data can be leveraged to support data-driven goal elicitation and customization.
Title: Illustrating the Gaps and Needs in the Training Support of Community Health Workers in India
Authors: family: Yadav
given: Deepika; family: Malik
given: Prerna; family: Dabas
given: Kirti; family: Singh
given: Pushpendra
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: CHW, ASHA, mHealth, ICTD, HCI4D

Abstract: In India and other developing countries, Community Health Workers (CHWs) provide the first line of care in delivering necessary maternal and child health services. In this work, we assess the training and skill-building needs of CHWs, through a mobile-based training intervention deployed for six months to 500 CHWs for conducting 144 training sessions in rural India. We qualitatively probed 1178 questions, asked by CHWs, during training sessions, and conducted a content analysis of the learning material provided to CHWs. Further, we interviewed 48 CHWs to understand the rationale of information seeking and perceptions of training needs. We present our understanding of the knowledge gaps of CHWs and how the current learning material and training methods are ineffective in addressing it. Our study presents design implications for HCI4D researchers for mobile learning platforms targeted towards CHWs. We also provide policy-level suggestions to improve the training of CHWs in India or a similar context.
Title: Religion and Women’s Intimate Health: Towards an Inclusive Approach to Healthcare
Authors: family: Mustafa
given: Maryam; family: Zaman
given: Kimia Tuz; family: Ahmad
given: Tallal; family: Batool
given: Amna; family: Ghazali
given: Masitah; family: Ahmed
given: Nova
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: health design, religion, menopause, women’s health, Islam, menarche

Abstract: We present findings from a three country study exploring the intersection between female intimate health and religious beliefs. Through a qualitative study with Muslim female populations in Pakistan, Bangladesh and Malaysia, three different Muslim majority contexts, we examine the deep impact Islamic beliefs have on female intimate health and well-being. Our study investigates the perceptions, attitudes and behaviours of Muslim women to their own intimate and sexual bodies through their experiences of menarche, marriage and reproduction and menopause. The intersection of religion and female sexual bodies and health is a neglected area within HCI and we highlight how inextricably specific Islamic values are linked with women’s reproductive health in Muslim communities. We further discuss the opportunities and challenges of designing technologies for religious, non-secular beliefs and values with the aim to improve intimate health practices amongst Muslim women and to broaden the scope of health design within HCI.
Title: KiTT - The Kinaesthetics Transfer Teacher: Design and Evaluation of a Tablet-based System to Promote the Learning of Ergonomic Patient Transfers
Authors: family: Dürr
given: Maximilian; family: Borowski
given: Marcel; family: Gröschel
given: Carla; family: Pfeil
given: Ulrike; family: Müller
given: Jens; family: Reiterer
given: Harald
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: kinaesthetics, tablet-based system, learning, nursing care

Abstract: Nurses frequently transfer patients as part of their daily work. However, manual patient transfers pose a major risk to nurses’ health. Although the Kinaesthetics care conception can help address this issue, existing support to learn the concept is low. We present KiTT, a tablet-based system, to promote the learning of ergonomic patient transfers based on the Kinaesthetics care conception. KiTT supports the training of Kinaesthetics-based patient transfers by two nurses. The nurses are guided by the phases (i) interactive instructions, (ii) training of transfer conduct, and (iii) feedback and reflection. We evaluated KiTT with 26 nursing-care students in a nursing-care school. Our results indicate that KiTT provides a good subjective support for the learning of Kinaesthetics. Our results also suggest that KiTT can promote the ergonomically correct conduct of patient transfers while providing a good user experience adequate to the nursing-school context, and reveal how KiTT can extend existing practices.
Title: Does Clickbait Actually Attract More Clicks? Three Clickbait Studies You Must Read
Authors: family: D. Molina
given: Maria; family: Sundar
given: S. Shyam; family: Rony
given: Md Main Uddin; family: Hassan
given: Naeemul; family: Le
given: Thai; family: Lee
given: Dongwon
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: clickbait, content perception, engagement, machine learning

Abstract: Studies show that users do not reliably click more often on headlines classified as clickbait by automated classifiers. Is this because the linguistic criteria (e.g., use of lists or questions) emphasized by the classifiers are not psychologically relevant in attracting interest, or because their classifications are confounded by other unknown factors associated with assumptions of the classifiers? We address these possibilities with three studies—a quasi-experiment using headlines classified as clickbait by three machine-learning models (Study 1), a controlled experiment varying the headline of an identical news story to contain only one clickbait characteristic (Study 2), and a computational analysis of four classifiers using real-world sharing data (Study 3). Studies 1 and 2 revealed that clickbait did not generate more curiosity than non-clickbait. Study 3 revealed that while some headlines generate more engagement, the detectors agreed on a classification only 47% of the time, raising fundamental questions about their validity.
Title: Human Perceptions on Moral Responsibility of AI: A Case Study in AI-Assisted Bail Decision-Making
Authors: family: Lima
given: Gabriel; family: Grgić-Hlača
given: Nina; family: Cha
given: Meeyoung
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Moral Judgment, COMPAS, Liability, Blame, Bail Decision-Making, Moral Responsibility, AI, Responsibility

Abstract: How to attribute responsibility for autonomous artificial intelligence (AI) systems’ actions has been widely debated across the humanities and social science disciplines. This work presents two experiments (N=200 each) that measure people’s perceptions of eight different notions of moral responsibility concerning AI and human agents in the context of bail decision-making. Using real-life adapted vignettes, our experiments show that AI agents are held causally responsible and blamed similarly to human agents for an identical task. However, there was a meaningful difference in how people perceived these agents’ moral responsibility; human agents were ascribed to a higher degree of present-looking and forward-looking notions of responsibility than AI agents. We also found that people expect both AI and human decision-makers and advisors to justify their decisions regardless of their nature. We discuss policy and HCI implications of these findings, such as the need for explainable AI in high-stakes scenarios.
Title: Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits
Authors: family: Richardson
given: Brianna; family: Garcia-Gathright
given: Jean; family: Way
given: Samuel F.; family: Thom
given: Jennifer; family: Cramer
given: Henriette
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: ML, algorithmic bias, ethics, fairness, user-centric evaluation, machine learning fairness, AI

Abstract: In order to support fairness-forward thinking by machine learning (ML) practitioners, fairness researchers have created toolkits that aim to transform state-of-the-art research contributions into easily-accessible APIs. Despite these efforts, recent research indicates a disconnect between the needs of practitioners and the tools offered by fairness research. By engaging 20 ML practitioners in a simulated scenario in which they utilize fairness toolkits to make critical decisions, this work aims to utilize practitioner feedback to inform recommendations for the design and creation of fair ML toolkits. Through the use of survey and interview data, our results indicate that though fair ML toolkits are incredibly impactful on users’ decision-making, there is much to be desired in the design and demonstration of fairness results. To support the future development and evaluation of toolkits, this work offers a rubric that can be used to identify critical components of Fair ML toolkits.
Title: Manipulating and Measuring Model Interpretability
Authors: family: Poursabzi-Sangdeh
given: Forough; family: Goldstein
given: Daniel G; family: Hofman
given: Jake M; family: Wortman Vaughan
given: Jennifer Wortman; family: Wallach
given: Hanna
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: interpretability, human-centered machine learning, machine-assisted decision making

Abstract: With machine learning models being increasingly used to aid decision making even in high-stakes domains, there has been a growing interest in developing interpretable models. Although many supposedly interpretable models have been proposed, there have been relatively few experimental studies investigating whether these models achieve their intended effects, such as making people more closely follow a model’s predictions when it is beneficial for them to do so or enabling them to detect when a model has made a mistake. We present a sequence of pre-registered experiments (N = 3, 800) in which we showed participants functionally identical models that varied only in two factors commonly thought to make machine learning models more or less interpretable: the number of features and the transparency of the model (i.e., whether the model internals are clear or black box). Predictably, participants who saw a clear model with few features could better simulate the model’s predictions. However, we did not find that participants more closely followed its predictions. Furthermore, showing participants a clear model meant that they were less able to detect and correct for the model’s sizable mistakes, seemingly due to information overload. These counterintuitive findings emphasize the importance of testing over intuition when developing interpretable models.
Title: Effects of Communication Directionality and AI Agent Differences in Human-AI Interaction
Authors: family: Ashktorab
given: Zahra; family: Dugan
given: Casey; family: Johnson
given: James; family: Pan
given: Qian; family: Zhang
given: Wei; family: Kumaravel
given: Sadhana; family: Campbell
given: Murray
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: social perception, human-AI interaction, collaboration, games

Abstract: In Human-AI collaborative settings that are inherently interactive, direction of communication plays a role in how users perceive their AI partners. In an AI-driven cooperative game with partially observable information, players (be it the AI or the human player) require their actions to be interpreted accurately by the other player to yield a successful outcome. In this paper, we investigate social perceptions of AI agents with various directions of communication in a cooperative game setting. We measure subjective social perceptions (rapport, intelligence, and likeability) of participants towards their partners when participants believe they are playing with an AI or with a human and the nature of the communication (responsiveness and leading roles). We ran a large scale study on Mechanical Turk (n=199) of this collaborative game and find significant differences in gameplay outcome and social perception across different AI agents, different directions of communication and when the agent is perceived to be an AI/Human. We find that the bias against the AI that has been demonstrated in prior studies varies with the direction of the communication and with the AI agent.