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检索条件"任意字段=Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems"

共 5313 条记录，以下是101-110 订阅

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Comparing Zealous and Restrained AI Recommendations in a Real-World human-AI Collaboration Task 23

Comparing Zealous and Restrained AI Recommendations in a Rea...

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chi conference on human factors in computing systems (chi)

作者： Xu, Chengyuan Lien, Kuo-chin Hollerer, Tobias Univ Calif Santa Barbara Santa Barbara CA USA Appen Sunnyvale CA USA

ISBN: (纸本)9781450394215

When designing an AI-assisted decision-making system, there is often a tradeoff between precision and recall in the AI's recommendations. We argue that careful exploitation of this tradeoff can harness the complementary strengths in the human-AI collaboration to significantly improve team performance. We investigate a real-world video anonymization task for which recall is paramount and more costly to improve. We analyze the performance of 78 professional annotators working with a) no AI assistance, b) a high-precision "restrained" AI, and c) a high-recall "zealous" AI in over 3,466 person-hours of annotation work. In comparison, the zealous AI helps human teammates achieve significantly shorter task completion time and higher recall. In a follow-up study, we remove AI assistance for everyone and find negative training effects on annotators trained with the restrained AI. These findings and our analysis point to important implications for the design of AI assistance in recall-demanding scenarios.

关键词： human-AI team AI-assisted decision making precision and recall real-world application empirical study computer vision face detection video annotation

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Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making 23

Competent but Rigid: Identifying the Gap in Empowering AI to...

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chi conference on human factors in computing systems (chi)

作者： Zheng, Chengbo Wu, Yuheng Shi, Chuhan Ma, Shuai Luo, Jiehui Ma, Xiaojuan Hong Kong Univ Sci & Technol Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China

ISBN: (纸本)9781450394215

Existing research on human-AI collaborative decision-making focuses mainly on the interaction between AI and individual decision-makers. There is a limited understanding of how AI may perform in group decision-making. This paper presents a wizard-of-oz study in which two participants and an AI form a committee to rank three English essays. One novelty of our study is that we adopt a speculative design by endowing AI equal power to humans in group decision-making. We enable the AI to discuss and vote equally with other human members. We fnd that although the voice of AI is considered valuable, AI still plays a secondary role in the group because it cannot fully follow the dynamics of the discussion and make progressive contributions. Moreover, the divergent opinions of our participants regarding an "equal AI" shed light on the possible future of human-AI relations.

关键词： human-AI collaboration group decision-making automated essay grading qualitative study

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Data, Data, Everywhere: Uncovering Everyday Data Experiences for People with Intellectual and Developmental Disabilities 23

Data, Data, Everywhere: Uncovering Everyday Data Experiences...

引用

chi conference on human factors in computing systems (chi)

作者： Wu, Keke Tran, Michelle H. Petersen, Emma Koushik, Varsha Szafr, Danielle Albers Univ N Carolina Chapel Hill NC 27515 USA Univ Colorado Boulder Boulder CO USA Colorado Coll Colorado Springs CO USA

ISBN: (纸本)9781450394215

Data is everywhere but may not be accessible to everyone. Conventional data visualization tools and guidelines often do not actively consider the specific needs and abilities of people with Intellectual and Developmental Disabilities (IDD), leaving them excluded from data-driven activities and vulnerable to ethical issues. To understand the needs and challenges people with IDD have with data, we conducted 15 semi-structured interviews with individuals with IDD and their caregivers. Our algorithmic interview approach situated data in the lived experiences of people with IDD to uncover otherwise hidden data encounters in their everyday life. Drawing on findings and observations, we characterize how they conceptualize data, when and where they use data, and what barriers exist when they interact with data. We use our results as a lens to reimagine the role of visualization in data accessibility and establish a critical near-term research agenda for cognitively accessible visualization.

关键词： human-subjects qualitative studies personal visual analytics

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Deep Learning Super-Resolution Network Facilitating Fiducial Tangibles on Capacitive Touchscreens 23

Deep Learning Super-Resolution Network Facilitating Fiducial...

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chi conference on human factors in computing systems (chi)

作者： Rusu, Marius Mayer, Sven Ludwig Maximilians Univ Munchen Munich Germany Munich Ctr Machine Learning MCML Munich Germany

ISBN: (纸本)9781450394215

Over the last few years, we have seen many approaches using tangibles to address the limited expressiveness of touchscreens. Mainstream tangible detection uses fiducial markers embedded in the tangibles. However, the coarse sensor size of capacitive touchscreens makes tangibles bulky, limiting their usefulness. We propose a novel deep-learning super-resolution network to facilitate fducial tangibles on capacitive touchscreens better. In detail, our network super-resolves the markers enabling of-the-shelf detection algorithms to track tangibles reliably. Our network generalizes to unseen marker sets, such as AprilTag, ArUco, and ARToolKit. Therefore, we are not limited to a fixed number of distinguishable objects and do not require data collection and network training for new fiducial markers. With extensive evaluation, including realworld users and five showcases, we demonstrate the applicability of our open-source approach on commodity mobile devices and further highlight the potential of tangibles on capacitive touchscreens.

关键词： human-computer interaction deep learning super resolution capacitive touchscreen

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"Piece it together": Insights from one year of engagement with electronics and programming for people with intellectual disabilities 23

"Piece it together": Insights from one year of engagement wi...

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chi conference on human factors in computing systems (chi)

作者： Ellis, Kirsten Kruesi, Lisa Ananthanarayan, Swamy Senaratne, Hashini Lindsay, Stephen Monash Univ Dept Human Ctr Comp Melbourne Vic Australia CSIRO Data61 Pullenvale Qld Australia Univ Glasgow Sch Comp Sci Glasgow Lanark Scotland

ISBN: (纸本)9781450394215

We present the results of one year spent engaging people living with intellectual disabilities with an electronics and programming package. The program was run in collaboration with a disability support organization and delivered by support workers. We evaluate key qualities of the package at three sites via ongoing communication and reflective interviews with five support workers, along with observation of sessions and contextual inquiry with eleven people with a range of disabilities. Our findings demonstrate the importance of physicality in enabling experiences by creating real-world analogues and supporting diverse group interactions;how groups support members' attention, motivating each other, and allow space for coping mechanisms;and participants' growing confidence and creativity in problem solving, and the emergence of self-directed activities. We discuss the importance of diverse repetition for skill development, how skills develop over the year, and pragmatic lessons for conducting a long-term research program with a disability support organization.

关键词： Accessibility intellectual disability making toolkits tangibles

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AeroRigUI: Actuated TUIs for Spatial Interaction using Rigging Swarm Robots on Ceilings in Everyday Space 23

AeroRigUI: Actuated TUIs for Spatial Interaction using Riggi...

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chi conference on human factors in computing systems (chi)

作者： Yu, Lilith Gao, Chenfeng Wu, David Nakagaki, Ken Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9781450394215

We present AeroRigUI, an actuated tangible UI for 3D spatial embodied interaction. Using strings controlled by self-propelled swarm robots with a reeling mechanism on ceiling surfaces, our approach enables rigging (control through strings) physical objects' position and orientation in the air. This can be applied to novel interactions in 3D space, including dynamic physical affordances, 3D information displays, and haptics. Utilizing the ceiling, an often underused room area, AeroRigUI can be applied for a range of applications such as room organization, data physicalization, and animated expressions. We demonstrate the applications based on our proof-of-concept prototype, which includes the hardware design of the rigging robots, named RigBots, and the software design for mid-air object control via interactive string manipulation. We also introduce technical evaluation and analysis of our approach prototype to address the hardware feasibility and safety. Overall, AeroRigUI enables a novel spatial and tangible UI system with great controllability and deployability.

关键词： Actuated Tangible User Interface Spatial User Interface Display human Robot Interaction

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fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks 23

fAIlureNotes: Supporting Designers in Understanding the Limi...

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chi conference on human factors in computing systems (chi)

作者： Moore, Steven Liao, Q. Vera Subramonyam, Hariharan Tech Univ Munich Munich Germany Microsoft Res Montreal PQ Canada Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781450394215

To design with AI models, user experience (UX) designers must assess the fit between the model and user needs. Based on user research, they need to contextualize the model's behavior and potential failures within their product-specific data instances and user scenarios. However, our formative interviews with ten UX professionals revealed that such a proactive discovery of model limitations is challenging and time-intensive. Furthermore, designers often lack technical knowledge of AI and accessible exploration tools, which challenges their understanding of model capabilities and limitations. In this work, we introduced a failure-driven design approach to AI, a workflow that encourages designers to explore model behavior and failure patterns early in the design process. The implementation of fAIlureNotes, a designer-centered failure exploration and analysis tool, supports designers in evaluating models and identifying failures across diverse user groups and scenarios. Our evaluation with UX practitioners shows that fAIlureNotes outperforms today's interactive model cards in assessing context-specific model performance.

关键词： human-Centered AI Pre-trained Models UX design

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GANravel: User-Driven Direction Disentanglement in Generative Adversarial Networks 23

GANravel: User-Driven Direction Disentanglement in Generativ...

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chi conference on human factors in computing systems (chi)

作者： Evirgen, Noyan Chen, Xiang 'Anthony' Univ Calif Los Angeles HCI Res Los Angeles CA 90024 USA

ISBN: (纸本)9781450394215

Generative adversarial networks (GANs) have many application over how to improve editing directions through disentanglement. areas including image editing, domain translation, missing data imputation, and support for creative work. However, GANs are considered 'black boxes'. Specifcally, the end-users have little control over how to improve editing directions through disentanglement. Prior work focused on new GAN architectures to disentangle editing directions. Alternatively, we propose GAN(RAVEL-a) user-driven direction disentanglement tool that complements the existing GAN architectures and allows users to improve editing directions iteratively. In two user studies with 16 participants each, GAN(RAVEL) users were able to disentangle directions and outperformed the state-of-the-art direction discovery baselines in disentanglement performance. In the second user study, GAN(RAVEL) was used in a creative task of creating dog memes and was able to create high-quality edited images and GIFs.

关键词： Generative Adversarial Networks Disentanglement Interactive systems Explainable-AI

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Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in human-AI Decision-Making 24

Dealing with Uncertainty: Understanding the Impact of Progno...

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chi conference on human factors in computing Sytems (chi)

作者： Salimzadeh, Sara He, Gaole Gadiraju, Ujwal Delft Univ Technol Delft Netherlands

ISBN: (纸本)9798400703300

While existing literature has explored and revealed several insights pertaining to the role of human factors (e.g., prior experience, domain knowledge) and attributes of AI systems (e.g., accuracy, trustworthiness), there is a limited understanding around how the important task characteristics of complexity and uncertainty shape human decision-making and human-AI team performance. In this work, we aim to address this research and empirical gap by systematically exploring how task complexity and uncertainty influence human-AI decision-making. Task complexity refers to the load of information associated with a task, while task uncertainty refers to the level of unpredictability associated with the outcome of a task. We conducted a between-subjects user study (N = 258) in the context of a trip-planning task to investigate the impact of task complexity and uncertainty on human trust and reliance on AI systems. Our results revealed that task complexity and uncertainty have a significant impact on user reliance on AI systems. When presented with complex and uncertain tasks, users tended to rely more on AI systems while demonstrating lower levels of appropriate reliance compared to tasks that were less complex and uncertain. In contrast, we found that user trust in the AI systems was not influenced by task complexity and uncertainty. Our findings can help inform the future design of empirical studies exploring human-AI decision-making. Insights from this work can inform the design of AI systems and interventions that are better aligned with the challenges posed by complex and uncertain tasks. Finally, the lens of diagnostic versus prognostic tasks can inspire the operationalization of uncertainty in human-AI decision-making studies.

关键词： Decision making

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Changes in Research Ethics, Openness, and Transparency in Empirical Studies between chi 2017 and chi 2022 23

Changes in Research Ethics, Openness, and Transparency in Em...

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chi conference on human factors in computing systems (chi)

作者： Niksirat, Kavous Salehzadeh Goswami, Lahari Rao, Pooja S. B. Tyler, James Silacci, Alessandro Aliyu, Sadiq Aebli, Annika Wacharamanotham, Chat Cherubini, Mauro Univ Lausanne Lausanne Switzerland HES SO Univ Appl Sci & Arts Western Switzerland Sch Management Fribourg Fribourg Switzerland Swansea Univ Swansea W Glam Wales

ISBN: (纸本)9781450394215

In recent years, various initiatives from within and outside the HCI field have encouraged researchers to improve research ethics, openness, and transparency in their empirical research. We quantify how the chi literature might have changed in these three aspects by analyzing samples of 118 chi 2017 and 127 chi 2022 papers-randomly drawn and stratified across conference sessions. We operationalized research ethics, openness, and transparency into 45 criteria and manually annotated the sampled papers. The results show that the chi 2022 sample was better in 18 criteria, but in the rest of the criteria, it has no improvement. The most noticeable improvements were related to research transparency (10 out of 17 criteria). We also explored the possibility of assisting the verification process by developing a proof-of-concept screening system. We tested this tool with eight criteria. Six of them achieved high accuracy and F1 score. We discuss the implications for future research practices and education. This paper and all supplementary materials are freely available at https://***/10.17605/***/n25d6.

关键词： replicability reproducibility transparency ethics open science data availability chi

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