huashen218/convxai-cia-dataset huggingface dataset:
Title: ‘It Is Not Always Discovery Time’: Four Pragmatic Approaches in Designing AI Systems
Authors: family: Windl
given: Maximiliane; family: Feger
given: Sebastian S.; family: Zijlstra
given: Lara; family: Schmidt
given: Albrecht; family: Wozniak
given: Pawel W.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: artificial intelligence, design, data work, process

Abstract: While systems that use Artificial Intelligence (AI) are increasingly becoming part of everyday technology use, we do not fully understand how AI changes design processes. A structured understanding of how designers work with AI is needed to improve the design process and educate future designers. To that end, we conducted interviews with designers who participated in projects which used AI. While past work focused on AI systems created by experienced designers, we focus on the perspectives of a diverse sample of interaction designers. Our results show that the design process of an interactive system is affected when AI is integrated and that design teams adapt their processes to accommodate AI. Based on our data, we contribute four approaches adopted by interaction designers working with AI: a priori, post-hoc, model-centric, and competence-centric. Our work contributes a pragmatic account of how design processes for AI systems are enacted.
Title: Designing Fair AI in Human Resource Management: Understanding Tensions Surrounding Algorithmic Evaluation and Envisioning Stakeholder-Centered Solutions
Authors: family: Park
given: Hyanghee; family: Ahn
given: Daehwan; family: Hosanagar
given: Kartik; family: Lee
given: Joonhwan
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Stakeholder-centered design, Explainable AI (XAI), Interpretability, Human Intervention, Fair and responsible AI, Future of work, Transparency, Algorithmic management, Artificial intelligence (AI), Human resource management

Abstract: Enterprises have recently adopted AI to human resource management (HRM) to evaluate employees’ work performance evaluation. However, in such an HRM context where multiple stakeholders are complexly intertwined with different incentives, it is problematic to design AI reflecting one stakeholder group's needs (e.g., enterprises, HR managers). Our research aims to investigate what tensions surrounding AI in HRM exist among stakeholders and explore design solutions to balance the tensions. By conducting stakeholder-centered participatory workshops with diverse stakeholders (including employees, employers/HR teams, and AI/business experts), we identified five major tensions: 1) divergent perspectives on fairness, 2) the accuracy of AI, 3) the transparency of the algorithm and its decision process, 4) the interpretability of algorithmic decisions, and 5) the trade-off between productivity and inhumanity. We present stakeholder-centered design ideas for solutions to mitigate these tensions and further discuss how to promote harmony among various stakeholders at the workplace.
Title: Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support
Authors: family: Kawakami
given: Anna; family: Sivaraman
given: Venkatesh; family: Cheng
given: Hao-Fei; family: Stapleton
given: Logan; family: Cheng
given: Yanghuidi; family: Qing
given: Diana; family: Perer
given: Adam; family: Wu
given: Zhiwei Steven; family: Zhu
given: Haiyi; family: Holstein
given: Kenneth
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: algorithm-assisted decision making, contextual inquiry, child welfare, decision support

Abstract: AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts. As public sector agencies begin to adopt ADS, it is critical that we understand workers’ experiences with these systems in practice. In this paper, we present findings from a series of interviews and contextual inquiries at a child welfare agency, to understand how they currently make AI-assisted child maltreatment screening decisions. Overall, we observe how workers’ reliance upon the ADS is guided by (1) their knowledge of rich, contextual information beyond what the AI model captures, (2) their beliefs about the ADS’s capabilities and limitations relative to their own, (3) organizational pressures and incentives around the use of the ADS, and (4) awareness of misalignments between algorithmic predictions and their own decision-making objectives. Drawing upon these findings, we discuss design implications towards supporting more effective human-AI decision-making.
Title: Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work
Authors: family: Zheng
given: Chengbo; family: Wang
given: Dakuo; family: Wang
given: April Yi; family: Ma
given: Xiaojuan
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: computational notebook, slides generation

Abstract: Creating presentation slides is a critical but time-consuming task for data scientists. While researchers have proposed many AI techniques to lift data scientists’ burden on data preparation and model selection, few have targeted the presentation creation task. Based on the needs identified from a formative study, this paper presents NB2Slides, an AI system that facilitates users to compose presentations of their data science work. NB2Slides uses deep learning methods as well as example-based prompts to generate slides from computational notebooks, and take users’ input (e.g., audience background) to structure the slides. NB2Slides also provides an interactive visualization that links the slides with the notebook to help users further edit the slides. A follow-up user evaluation with 12 data scientists shows that participants believed NB2Slides can improve efficiency and reduces the complexity of creating slides. Yet, participants questioned the future of full automation and suggested a human-AI collaboration paradigm.
Title: Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Authors: family: Lai
given: Vivian; family: Carton
given: Samuel; family: Bhatnagar
given: Rajat; family: Liao
given: Q. Vera; family: Zhang
given: Yunfeng; family: Tan
given: Chenhao
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Abstract: Despite impressive performance in many benchmark datasets, AI models can still make mistakes, especially among out-of-distribution examples. It remains an open question how such imperfect models can be used effectively in collaboration with humans. Prior work has focused on AI assistance that helps people make individual high-stakes decisions, which is not scalable for a large amount of relatively low-stakes decisions, e.g., moderating social media comments. Instead, we propose conditional delegation as an alternative paradigm for human-AI collaboration where humans create rules to indicate trustworthy regions of a model. Using content moderation as a testbed, we develop novel interfaces to assist humans in creating conditional delegation rules and conduct a randomized experiment with two datasets to simulate in-distribution and out-of-distribution scenarios. Our study demonstrates the promise of conditional delegation in improving model performance and provides insights into design for this novel paradigm, including the effect of AI explanations.
Title: Unpacking Practitioners’ Attitudes Towards Codifications of Design Knowledge for Voice User Interfaces
Authors: family: Khemani
given: Krishika Haresh; family: Reeves
given: Stuart
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: industry practitioners, voice user interfaces, Design guidelines

Abstract: Recent HCI research has sought to develop guidelines—‘heuristics’, ‘best practices’, ‘principles’ and so on—for voice user interfaces (VUI) to aid both practitioners and researchers in improving the quality of VUI-based design. However, limited research is available on how such design knowledge is conceptualised and used by industry practitioners. We present a small interview-based study conducted with 9 experienced VUI industry practitioners. Their concerns range from terminological challenges associated with VUI design knowledge, the role of codifications of such knowledge like design guidelines alongside their practical design work, through to their views on the value of ‘harmonisation’ of VUI design knowledge. Given the complex—albeit preliminary—picture that emerges, we argue for HCI's deeper consideration of how design knowledge meshes with the contingencies of practice, so that VUI design knowledge—such as design guidelines developed in HCI—delivers the most potential value for industry practice.
Title: Designing for Speech Practice Systems: How Do User-Controlled Voice Manipulation and Model Speakers Impact Self-Perceptions of Voice?
Authors: family: Orii
given: Lisa; family: Ogawa
given: Nami; family: Hatada
given: Yuji; family: Narumi
given: Takuji
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: presentation training, voice manipulation, speech examples, self-perception, public speaking

Abstract: Can you speak the way you desire without feeling the pressure to conform to standards of speaking? In this study, we investigated the impact of user-controlled voice manipulation and listening to recordings of model speakers on self-perceptions of voice and speech. Quantitative analysis showed that there was a significant improvement in the perceived confidence of tone by listening to model speakers, but there were no significant improvements due to voice manipulation. Qualitative analysis of interviews revealed that participants responded positively to the visual and auditory feedback provided by the voice manipulation software. The participants also evaluated the quality of model speakers to decide whether or not they wanted to refer to them for speech practice. Based on the results of these analyses, we summarized the design implications for a speech practice system that would allow further investigation of the impact of the system on self-perceptions of speech performance.
Title: Great Chain of Agents: The Role of Metaphorical Representation of Agents in Conversational Crowdsourcing
Authors: family: Jung
given: Ji-Youn; family: Qiu
given: Sihang; family: Bozzon
given: Alessandro; family: Gadiraju
given: Ujwal
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Trust, Crowdsourcing, Conceptual metaphors, Great chain of being, Engagement, Human-agent interaction, Human-AI interaction, Conversational agent

Abstract: Conversational agents are being widely adopted across several domains to serve a variety of purposes ranging from providing intelligent assistance to companionship. Recent literature has shown that users develop intuitive folk theories and a metaphorical understanding of conversational agents (CAs) due to the lack of a mental model of the agents. However, investigation of metaphorical agent representation in the HCI community has mainly focused on the human level, despite non-human metaphors for agents being prevalent in the real world. We adopted Lakoff and Turner’s ‘Great Chain of Being’ framework to systematically investigate the impact of using non-human metaphors to represent conversational agents on worker engagement in crowdsourcing marketplaces. We designed a text-based conversational agent that assists crowd workers in task execution. Through a between-subjects experimental study (N = 341), we explored how different human and non-human metaphors affect worker engagement, the perceived cognitive load of workers, intrinsic motivation, and their trust in the agents. Our findings bridge the gap of how users experience CAs with non-human metaphors in the context of conversational crowdsourcing.
Title: “Rewind to the Jiggling Meat Part”: Understanding Voice Control of Instructional Videos in Everyday Tasks
Authors: family: Zhao
given: Yaxi; family: Jaber
given: Razan; family: McMillan
given: Donald; family: Munteanu
given: Cosmin
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Voice-Based Navigation, Conversational Interaction, Non-Linear Instructional Video, Wizard-of-Oz

Abstract: Voice interaction has long been envisioned as enabling users to transform physical interaction into hands-free, such as allowing fine-grained control of instructional videos without physically disengaging from the task at hand. While significant engineering advances have brought us closer to this ideal, we do not fully understand the user requirements for voice interactions that should be supported in such contexts. This paper presents an ecologically-valid wizard-of-oz elicitation study exploring realistic user requirements for an ideal instructional video playback control while cooking. Through the analysis of the issued commands and performed actions during this non-linear and complex task, we identify (1) patterns of command formulation, (2) challenges for design, and (3) how task and voice-based commands are interwoven in real-life. We discuss implications for the design and research of voice interactions for navigating instructional videos while performing complex tasks.
Title: Monitoring Screen Time or Redesigning It? Two Approaches to Supporting Intentional Social Media Use
Authors: family: Zhang
given: Mingrui Ray; family: Lukoff
given: Kai; family: Rao
given: Raveena; family: Baughan
given: Amanda; family: Hiniker
given: Alexis
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: twitter, mobile phone, social media, digital wellbeing, sense of agency

Abstract: Existing designs helping people manage their social media use include: 1) external supports that monitor and limit use; 2) internal supports that change the interface itself. Here, we design and deploy Chirp, a mobile Twitter client, to independently examine how users experience external and internal supports. To develop Chirp, we identified 16 features that influence users’ sense of agency on Twitter through a survey of 129 participants and a design workshop. We then conducted a four-week within-subjects deployment with 31 participants. Our internal supports (including features to filter tweets and inform users when they have exhausted new content) significantly increased users’ sense of agency, while our external supports (a usage dashboard and nudges to close the app) did not. Participants valued our internal supports and said that our external supports were for “other people.” Our findings suggest that design patterns promoting agency may serve users better than screen time tools.
Title: Squeezy-Feely: Investigating Lateral Thumb-Index Pinching as an Input Modality
Authors: family: Schmitz
given: Martin; family: Günther
given: Sebastian; family: Schön
given: Dominik; family: Müller
given: Florian
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Input, Pinching, User Studies, Deformation, Mixed Reality, Thumb-to-finger

Abstract: From zooming on smartphones and mid-air gestures to deformable user interfaces, thumb-index pinching grips are used in many interaction techniques. However, there is still a lack of systematic understanding of how the accuracy and efficiency of such grips are affected by various factors such as counterforce, grip span, and grip direction. Therefore, in this paper, we contribute an evaluation (N = 18) of thumb-index pinching performance in a visual targeting task using scales up to 75 items. As part of our findings, we conclude that the pinching interaction between the thumb and index finger is a promising modality also for one-dimensional input on higher scales. Furthermore, we discuss and outline implications for future user interfaces that benefit from pinching as an additional and complementary interaction modality.
Title: The Digital Landscape of Nudging: A Systematic Literature Review of Empirical Research on Digital Nudges
Authors: family: Bergram
given: Kristoffer; family: Djokovic
given: Marija; family: Bezençon
given: Valéry; family: Holzer
given: Adrian
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: digital nudges, personalization, nudge patterns, digital nudging, choice architecture, interconnectedness, review

Abstract: Research output related to digital nudging has increased ten-fold over the last five years. Nudging in the digital realm differs from its analog counterpart in important ways. For instance, online, choice architectures can be interconnected and personalized using real time data. In the face of this development, it is crucial to understand the current state of the literature and to map out possible research gaps. This paper addresses this issue and provides a systematic review of empirical studies where digital nudges have been evaluated. The systematic review covers 73 peer-reviewed papers containing 109 separate studies where 231 digital nudges have been evaluated. Our results lead to nine open research questions to be addressed in the future by the research community.
Title: Design is Worth a Thousand Words: The Effect of Digital Interaction Design on Picture-Prompted Reminiscence
Authors: family: Axtell
given: Benett; family: Saryazdi
given: Raheleh; family: Munteanu
given: Cosmin
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: digital storytelling, reminiscence, Digital picture interactions

Abstract: Interactions with our personal and family pictures are essential to continued social reminiscence, leading to long-term benefits, including reduced social isolation. Previous research has identified how designs of digital picture tools fall short of physical options specifically in terms of reminiscence. However, the relative prompting abilities of different digital interactions, including the types of memories prompted like external facts or person-centred memories, have not yet been explored. To investigate this, we present a controlled study of the memories prompted by three digital picture interactions (slideshow, gallery, and tabletop) on personal touchscreen devices. We find differences in how these tools and the interactions they support prompt reminiscence. In particular, gallery views prompt significantly fewer memories than either the tabletop or slideshow. Slideshows prompt significantly more external, factual memories, but not more person-centred memories, which are key to reminiscence. This has implications for the overall social usability of digital picture interactions.
Title: Understanding Multi-Device Usage Patterns: Physical Device Configurations and Fragmented Workflows
Authors: family: Yuan
given: Ye; family: Riche
given: Nathalie; family: Marquardt
given: Nicolai; family: Nicholas
given: Molly Jane; family: Seyed
given: Teddy; family: Romat
given: Hugo; family: Lee
given: Bongshin; family: Pahud
given: Michel; family: Goldstein
given: Jonathan; family: Vishkaie
given: Rojin; family: Holz
given: Christian; family: Hinckley
given: Ken
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: cross-device computing, distributed user interfaces, multi-device

Abstract: To better ground technical (systems) investigation and interaction design of cross-device experiences, we contribute an in-depth survey of existing multi-device practices, including fragmented workflows across devices and the way people physically organize and configure their workspaces to support such activity. Further, this survey documents a historically significant moment of transition to a new future of remote work, an existing trend dramatically accelerated by the abrupt switch to work-from-home (and having to contend with the demands of home-at-work) during the COVID-19 pandemic. We surveyed 97 participants, and collected photographs of home setups and open-ended answers to 50 questions categorized in 5 themes. We characterize the wide range of multi-device physical configurations and identify five usage patterns, including: partitioning tasks, integrating multi-device usage, cloning tasks to other devices, expanding tasks and inputs to multiple devices, and migrating between devices. Our analysis also sheds light on the benefits and challenges people face when their workflow is fragmented across multiple devices. These insights have implications for the design of multi-device experiences that support people’s fragmented workflows.
Title: ScalAR: Authoring Semantically Adaptive Augmented Reality Experiences in Virtual Reality
Authors: family: Qian
given: Xun; family: He
given: Fengming; family: Hu
given: Xiyun; family: Wang
given: Tianyi; family: Ipsita
given: Ananya; family: Ramani
given: Karthik
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Virtual Reality, Semantic Understanding, Immersive Authoring, Augmented Reality, Adaptation

Abstract: Augmented Reality (AR) experiences tightly associate virtual contents with environmental entities. However, the dissimilarity of different environments limits the adaptive AR content behaviors under large-scale deployment. We propose ScalAR, an integrated workflow enabling designers to author semantically adaptive AR experiences in Virtual Reality (VR). First, potential AR consumers collect local scenes with a semantic understanding technique. ScalAR then synthesizes numerous similar scenes. In VR, a designer authors the AR contents’ semantic associations and validates the design while being immersed in the provided scenes. We adopt a decision-tree-based algorithm to fit the designer’s demonstrations as a semantic adaptation model to deploy the authored AR experience in a physical scene. We further showcase two application scenarios authored by ScalAR and conduct a two-session user study where the quantitative results prove the accuracy of the AR content rendering and the qualitative results show the usability of ScalAR.
Title: An Exploratory Study of Sharing Strategic Programming Knowledge
Authors: family: Arab
given: Maryam; family: LaToza
given: Thomas D.; family: Liang
given: Jenny; family: Ko
given: Amy J.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Programming strategies, Knowledge sharing

Abstract: In many domains, strategic knowledge is documented and shared through checklists and handbooks. In software engineering, however, developers rarely share strategic knowledge for approaching programming problems, in contrast to other artifacts and despite its importance to productivity and success. To understand barriers to sharing, we simulated a programming strategy knowledge-sharing platform, asking experienced developers to articulate a programming strategy and others to use these strategies while providing feedback. Throughout, we asked strategy authors and users to reflect on the challenges they faced. Our analysis revealed that developers could share strategic knowledge. However, they struggled in choosing a level of detail and understanding the diversity of the potential audience. While authors required substantial feedback, users struggled to give it and authors to interpret it. Our results suggest that sharing strategic knowledge differs from sharing code and raises challenging questions about how knowledge-sharing platforms should support search and feedback.