huashen218/convxai-cia-dataset huggingface dataset:
Title: ZenVR: Design Evaluation of a Virtual Reality Learning System for Meditation
Authors: family: Feinberg
given: Rachel R.; family: Lakshmi
given: Udaya; family: Golino
given: Matthew J.; family: Arriaga
given: Rosa I.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Virtual reality, wellness, interaction design, self-guided learning, meditation

Abstract: Meditation has become a popular option to manage stress. Though studies examine technologies to assist in meditation, few explore how technology supports development of such skills for independent practice. From a two-phase mixed-methods study, we contribute learner-centered insights from 36 participants in a virtual reality environment designed to teach meditation skills to novices. In Phase I, we gathered affective and behavioral learner needs from 21 meditation novices, experts, and instructors to synthesize insights for learning. We then designed ZenVR: an interactive system to deliver an eight-lesson meditation curriculum to support learners’ progress. In Phase II, we conducted a 6-week longitudinal lab-based evaluation with 15 novice meditation learners. We found statistically significant improvements in mindfulness and self-reported meditation ability. Their insights from a self-managed practice, two weeks after the study ended, offered opportunities to understand how technology can be designed to offer progressive support without creating dependence in technology-mediated meditation practice.
Title: Understanding the impact of explanations on advice-taking: a user study for AI-based clinical Decision Support Systems
Authors: family: Panigutti
given: Cecilia; family: Beretta
given: Andrea; family: Giannotti
given: Fosca; family: Pedreschi
given: Dino
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Clinical Decision Support System, eXplainable AI, Advice-taking, Behavioral intention, XAI, Trust, User Study, HCI

Abstract: The field of eXplainable Artificial Intelligence (XAI) focuses on providing explanations for AI systems’ decisions. XAI applications to AI-based Clinical Decision Support Systems (DSS) should increase trust in the DSS by allowing clinicians to investigate the reasons behind its suggestions. In this paper, we present the results of a user study on the impact of advice from a clinical DSS on healthcare providers’ judgment in two different cases: the case where the clinical DSS explains its suggestion and the case it does not. We examined the weight of advice, the behavioral intention to use the system, and the perceptions with quantitative and qualitative measures. Our results indicate a more significant impact of advice when an explanation for the DSS decision is provided. Additionally, through the open-ended questions, we provide some insights on how to improve the explanations in the diagnosis forecasts for healthcare assistants, nurses, and doctors.
Title: GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information
Authors: family: Dang
given: Hai; family: Mecke
given: Lukas; family: Buschek
given: Daniel
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: generative adversarial network, interactive AI, image manipulation, dataset, user study

Abstract: We investigate how multiple sliders with and without feedforward visualizations influence users’ control of generative models. In an online study (N=138), we collected a dataset of people interacting with a generative adversarial network (StyleGAN2) in an image reconstruction task. We found that more control dimensions (sliders) significantly increase task difficulty and user actions. Visual feedforward partly mitigates this by enabling more goal-directed interaction. However, we found no evidence of faster or more accurate task performance. This indicates a tradeoff between feedforward detail and implied cognitive costs, such as attention. Moreover, we found that visualizations alone are not always sufficient for users to understand individual control dimensions. Our study quantifies fundamental UI design factors and resulting interaction behavior in this context, revealing opportunities for improvement in the UI design for interactive applications of generative models. We close by discussing design directions and further aspects.
Title: UX Research on Conversational Human-AI Interaction: A Literature Review of the ACM Digital Library
Authors: family: Zheng
given: Qingxiao; family: Tang
given: Yiliu; family: Liu
given: Yiren; family: Liu
given: Weizi; family: Huang
given: Yun
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Conversational AI, Conversational Agent, UX Research, Literature Review, Chatbot

Abstract: Early conversational agents (CAs) focused on dyadic human-AI interaction between humans and the CAs, followed by the increasing popularity of polyadic human-AI interaction, in which CAs are designed to mediate human-human interactions. CAs for polyadic interactions are unique because they encompass hybrid social interactions, i.e., human-CA, human-to-human, and human-to-group behaviors. However, research on polyadic CAs is scattered across different fields, making it challenging to identify, compare, and accumulate existing knowledge. To promote the future design of CA systems, we conducted a literature review of ACM publications and identified a set of works that conducted UX (user experience) research. We qualitatively synthesized the effects of polyadic CAs into four aspects of human-human interactions, i.e., communication, engagement, connection, and relationship maintenance. Through a mixed-method analysis of the selected polyadic and dyadic CA studies, we developed a suite of evaluation measurements on the effects. Our findings show that designing with social boundaries, such as privacy, disclosure, and identification, is crucial for ethical polyadic CAs. Future research should also advance usability testing methods and trust-building guidelines for conversational AI.
Title: The Trusted Listener: The Influence of Anthropomorphic Eye Design of Social Robots on User's Perception of Trustworthiness
Authors: family: Zhu
given: Xinyu; family: Zhang
given: Xingguo; family: Chen
given: Zinan; family: Dong
given: Zhanxun; family: Gu
given: Zhenyu; family: Chang
given: Danni
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: User's trustworthiness perception, Robot eye design, Social robot, Anthropomorphic design, Human-robot interaction

Abstract: Nowadays, social robots have become human's important companions. The anthropomorphic features of robots, which are important in building natural user experience and trustable human-robot partnership, have attracted increasing attention. Among these features, eyes attract most audience's attention and are particularly important. This study aims to investigate the influence of robot eye design on users’ trustworthiness perception. Specifically, a simulation robot model was developed. Three sets of experiments involving sixty-six participants were conducted to investigate the effects of (i) visual complexity of eye design, (ii) blink rate, and (iii) gaze aversion of social robots on users’ perceived trustworthiness. Results indicate that high visual complexity and gaze aversion lead to higher perceived trustworthiness and reveal a positive correlation between the perceived anthropomorphic effect of eye design and users’ perceived trust, while a non-significant effect of blink rate has been found. Preliminary suggestions are provided for the design of social robots in future works.
Title: Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science
Authors: family: Cambo
given: Scott Allen; family: Gergle
given: Darren
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Computational reflexivity, model positionality, human-centered data science, critical data studies, position mining, human-centered machine learning, annotator fingerprinting, data science

Abstract: Data science and machine learning provide indispensable techniques for understanding phenomena at scale, but the discretionary choices made when doing this work are often not recognized. Drawing from qualitative research practices, we describe how the concepts of positionality and reflexivity can be adapted to provide a framework for understanding, discussing, and disclosing the discretionary choices and subjectivity inherent to data science work. We first introduce the concepts of model positionality and computational reflexivity that can help data scientists to reflect on and communicate the social and cultural context of a model’s development and use, the data annotators and their annotations, and the data scientists themselves. We then describe the unique challenges of adapting these concepts for data science work and offer annotator fingerprinting and position mining as promising solutions. Finally, we demonstrate these techniques in a case study of the development of classifiers for toxic commenting in online communities.
Title: (Re)Politicizing Digital Well-Being: Beyond User Engagements
Authors: family: Docherty
given: Niall; family: Biega
given: Asia J.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: power, well-being, measurement, user engagement, time well-spent

Abstract: The psychological costs of the attention economy are often considered through the binary of harmful design and healthy use, with digital well-being chiefly characterised as a matter of personal responsibility. This article adopts an interdisciplinary approach to highlight the empirical, ideological, and political limits of embedding this individualised perspective in computational discourses and designs of digital well-being measurement. We will reveal well-being to be a culturally specific and environmentally conditioned concept and will problematize user engagement as a universal proxy for well-being. Instead, the contributing factors of user well-being will be located in environing social, cultural, and political conditions far beyond the control of individual users alone. In doing so, we hope to reinvigorate the issue of digital well-being measurement as a nexus point of political concern, through which multiple disciplines can study experiences of digital ill as symptomatic of wider social inequalities and (capitalist) relations of power.
Title: The Village: Infrastructuring Community-based Mentoring to Support Adults Experiencing Poverty
Authors: family: Dillahunt
given: Tawanna R; family: Lu
given: Alex Jiahong; family: Israni
given: Aarti; family: Lodha
given: Ruchita; family: Brewer
given: Savana; family: Robinson
given: Tiera S; family: Wilson
given: Angela Brown; family: Wheeler
given: Earnest
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: economic mobility, infrastructuring, social support, community-based participatory research, community-based mentorship, infrastructure, mentorship

Abstract: Mentorship and other social and relational support have been vital to poverty alleviation and transformative change. It is crucial to understand the underlying factors in the success of mentoring models and subsequent programs to support them. Thus, we conducted a mixed-methods study consisting of longitudinal surveys of community participants followed by semi-structured interviews with 28 community members, eight mentors, and two coaches participating in a community-based mentorship program. Drawing from community-based participatory research in partnership with a non-profit located in a Midwestern United States (U.S.) city, we unpack how the program supported self-sufficiency and economic mobility among adults experiencing financial hardships. Through an infrastructural lens, we attend to individuals’ infrastructuring work in social support, flexibility, and trust to support a “village” model of community-based mentorship. Our results show how the village model differs from traditional mentorship models that assume dyadic, one-to-one, often didactic, and hierarchical relationships (e.g., expert and protégé, adult and child) and are used primarily in the workplace and educational settings. The village mentorship model advocates for less hierarchical and more balanced relationships in non-institutional settings and flexible communication and technological needs. We discuss new research opportunities and design strategies for rethinking technology-mediated mentorship to support poverty-stricken adults in the U.S.
Title: Unpacking the Complexities of Community-led Violence Prevention Work
Authors: family: Erete
given: Sheena; family: Dickinson
given: Jessa; family: Gonzalez
given: Alejandra C.; family: Rankin
given: Yolanda A.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: public safety, support tools, community-led violence prevention, community justice initiatives, assets-based design

Abstract: With increased public scrutiny of policing and growing calls for community-based violence prevention, street outreach programs that hire residents to mediate conflicts in their neighborhoods are gaining support. To understand how street outreach workers (SOWs) use information and communication technologies (ICTs) and how they envision future ICTs that better support their work, we interviewed 25 SOWs across three organizations. Results suggest that SOWs leverage ICTs to: 1) identify and mediate conflict; 2) support collaboration and teamwork; and 3) invoke community connections and trust. SOWs posit that new ICTs could provide a seamless infrastructure for communication among SOWs and between community members, assist with training to sharpen conflict negotiation skills, and provide insight on effective conflict mediation strategies.
Title: Putting the Waz on Social Media: Infrastructuring Online Islamic Counterpublic through Digital Sermons in Bangladesh
Authors: family: Rifat
given: Mohammad Rashidujjaman; family: Prottoy
given: Hasan Mahmud; family: Ahmed
given: Syed Ishtiaque
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: politics, counterpublic, waz, Islamic media, content creation, infrastructuring, grassroots

Abstract: While the presence of religious content is rapidly increasing over digital media, the HCI literature on digital media production has remained mostly limited by its focus on secular contents and analyses. Hence, the production, politics, and impact of such religious videos from the Global South have remained understudied in HCI. In this paper, we shed light on this topic through our nine-month-long ethnographic study on the production, sharing, and consumption of Islamic sermon videos (locally known as Waz) in Bangladesh. We report how faith, informal learning, local collaboration, creativity, and care play crucial roles in creating Islamic sermon videos and their proliferation online. We discuss how the sermon videos create a religious counterpublic in Bangladesh. We further discuss how such faith-based media production makes important lessons pertinent to the national grassroots politics in the Global South, politics of social media platforms, and HCI4D scholarship.
Title: ‘ShishuShurokkha’: A Transformative Justice Approach for Combating Child Sexual Abuse in Bangladesh
Authors: family: Sultana
given: Sharifa; family: Pritha
given: Sadia Tasnuva; family: Tasnim
given: Rahnuma; family: Das
given: Anik; family: Akter
given: Rokeya; family: Hasan
given: Shaid; family: Alam
given: S.M. Raihanul; family: Kabir
given: Muhammad Ashad; family: Ahmed
given: Syed Ishtiaque
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Social Media, Gender Harassment, Transformative Justice, ShishuShurokkha, Community Driven Design, Shame, Solidarity, Bangladesh, Child Sexual abuse

Abstract: The challenge of designing against child sexual abuse becomes more complicated in conservative societies where talking about sex is tabooed. Our mix-method study, comprised of an online survey, five FGDs, and 20 semi-structured interviews in Bangladesh, investigates the common nature, location, and time of the abuse, post-incident support, and possible combating strategies. Besides revealing important facts, our findings highlight the need of decentering the design from the victims (children and/or guardians) to the community. Hence, building on the theory of transformative justice, we prototyped and evaluated ‘ShishuShurokkha’ – an online tool that involves the whole community by allowing anonymous bystander reporting, visualizing case-maps, connecting with legal, medical, and social support, and raising awareness. The evaluation of ShishuShurokkha shows the promise for such a communal approach toward combating child sexual abuse, and highlights the needs for sincere involvement of the government, NGOs, the legal, educational, and religious services in this.
Title: Owning Mistakes Sincerely: Strategies for Mitigating AI Errors
Authors: family: Mahmood
given: Amama; family: Fung
given: Jeanie W; family: Won
given: Isabel; family: Huang
given: Chien-Ming
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: blame assignment, error mitigation, voice interactions, Human-AI interaction, apologies

Abstract: Interactive AI systems such as voice assistants are bound to make errors because of imperfect sensing and reasoning. Prior human-AI interaction research has illustrated the importance of various strategies for error mitigation in repairing the perception of an AI following a breakdown in service. These strategies include explanations, monetary rewards, and apologies. This paper extends prior work on error mitigation by exploring how different methods of apology conveyance may affect people’s perceptions of AI agents; we report an online study (N=37) that examines how varying the sincerity of an apology and the assignment of blame (on either the agent itself or others) affects participants’ perceptions and experience with erroneous AI agents. We found that agents that openly accepted the blame and apologized sincerely for mistakes were thought to be more intelligent, likeable, and effective in recovering from errors than agents that shifted the blame to others.
Title: What’s the Appeal? Perceptions of Review Processes for Algorithmic Decisions
Authors: family: Lyons
given: Henrietta; family: Wijenayake
given: Senuri; family: Miller
given: Tim; family: Velloso
given: Eduardo
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: and transparency, algorithmic fairness, reviewability, algorithmic decision-making, contestability, accountability

Abstract: If you were significantly impacted by an algorithmic decision, how would you want the decision to be reviewed? In this study, we explore perceptions of review processes for algorithmic decisions that differ across three dimensions: the reviewer, how the review is conducted, and how long the review takes. Using a choice-based conjoint analysis we find that people prefer review processes that provide for human review, the ability to participate in the review process, and a timely outcome. Using a survey, we find that people also see human review that provides for participation to be the fairest review process. Our qualitative analysis indicates that the fairest review process provides the greatest likelihood of a favourable outcome, an opportunity for the decision subject and their situation to be fully and accurately understood, human involvement, and dignity. These findings have implications for the design of contestation procedures and also the design of algorithmic decision-making processes.
Title: “Look! It’s a Computer Program! It’s an Algorithm! It’s AI!”: Does Terminology Affect Human Perceptions and Evaluations of Algorithmic Decision-Making Systems?
Authors: family: Langer
given: Markus; family: Hunsicker
given: Tim; family: Feldkamp
given: Tina; family: König
given: Cornelius J.; family: Grgić-Hlača
given: Nina
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: replicability, research methodology, terminology, human-centered AI

Abstract: In the media, in policy-making, but also in research articles, algorithmic decision-making (ADM) systems are referred to as algorithms, artificial intelligence, and computer programs, amongst other terms. We hypothesize that such terminological differences can affect people’s perceptions of properties of ADM systems, people’s evaluations of systems in application contexts, and the replicability of research as findings may be influenced by terminological differences. In two studies (N = 397, N = 622), we show that terminology does indeed affect laypeople’s perceptions of system properties (e.g., perceived complexity) and evaluations of systems (e.g., trust). Our findings highlight the need to be mindful when choosing terms to describe ADM systems, because terminology can have unintended consequences, and may impact the robustness and replicability of HCI research. Additionally, our findings indicate that terminology can be used strategically (e.g., in communication about ADM systems) to influence people’s perceptions and evaluations of these systems.
Title: Whose AI Dream? In search of the aspiration in data annotation.
Authors: family: Wang
given: Ding; family: Prabhat
given: Shantanu; family: Sambasivan
given: Nithya
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: future of work, qualitative study, data annotation, AI labour

Abstract: Data is fundamental to AI/ML models. This paper investigates the work practices concerning data annotation as performed in the industry, in India. Previous human-centred investigations have largely focused on annotators’ subjectivity, bias and efficiency. We present a wider perspective of the data annotation: following a grounded approach, we conducted three sets of interviews with 25 annotators, 10 industry experts and 12 ML/AI practitioners. Our results show that the work of annotators is dictated by the interests, priorities and values of others above their station. More than technical, we contend that data annotation is a systematic exercise of power through organizational structure and practice. We propose a set of implications for how we can cultivate and encourage better practice to balance the tension between the need for high quality data at low cost and the annotators’ aspiration for well-being, career perspective, and active participation in building the AI dream.
Title: The CAT Effect: Exploring the Impact of Casual Affective Triggers on Online Surveys’ Response Rates
Authors: family: Chounta
given: Irene-Angelica; family: Nolte
given: Alexander
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: affect, user experience, online surveys, response rate

Abstract: We explore the impact of Casual Affective Triggers (CAT) on response rates of online surveys. As CAT, we refer to objects that can be included in survey participation invitations and trigger participants’ affect. The hypothesis is that participants who receive CAT-enriched invitations are more likely to respond to a survey. We conducted a study where the control condition received invitations without affective triggers, and the experimental condition received CAT-enriched invitations. We differentiated the triggers within the experimental condition: one-third of the population received a personalized invitation, one-third received a picture of the surveyor’s cat, and one-third received both. We followed up with a survey to validate our findings. Our results suggest that CATs have a positive impact on response rates. We did not find CATs to induce response bias.