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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 5126 条记录，以下是291-300 订阅

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Data Probes as Boundary Objects for Technology Policy Design: Demystifying Technology for Policymakers and Aligning Stakeholder Objectives in Rideshare Gig Work 24

Data Probes as Boundary Objects for Technology Policy Design...

引用

chi conference on human factors in computing Sytems (chi)

作者： Zhang, Angie Rana, Rocita Boltz, Alexander Dubal, Veena Lee, Min Kyung Univ Texas Austin Sch Informat Austin TX 78712 USA Univ Washington Human Ctr Design & Engn Seattle WA USA Univ Calif Irvine Sch Law Irvine CA USA

ISBN: (纸本)9798400703300

Despite the evidence of harm that technology can inflict, commensurate policymaking to hold tech platforms accountable still lags. This is pertinent to app-based gig workers, where unregulated algorithms continue to dictate their work, often with little human recourse. While past HCI literature has investigated workers experiences under algorithmic management and how to design interventions, rarely are the perspectives of stakeholders who inform or craft policy sought. To bridge this, we propose using data probes-interactive visualizations of workers data that show the impact of technology practices on people-exploring them in 12 semi-structured interviews with policy informers, (driver-)organizers, litigators, and a lawmaker in the rideshare space. We show how data probes act as boundary objects to assist stakeholder interactions, demystify technology for policymakers, and support worker collective action. We discuss the potential for data probes as training tools for policymakers, and considerations around data access and worker risks when using data probes.

关键词： Gig Work Policymaking Data Probes Algorithmic Management

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Harnessing Biomedical Literature to Calibrate Clinicians' Trust in AI Decision Support systems 23

Harnessing Biomedical Literature to Calibrate Clinicians' Tr...

引用

chi conference on human factors in computing systems (chi)

作者： Yang, Qian Hao, Yuexing Quan, Kexin Yang, Stephen Zhao, Yiran Kuleshov, Volodymyr Wang, Fei Cornell Univ Ithaca NY 14850 USA Univ Calif San Diego San Diego CA 92103 USA Cornell Univ New York NY 10021 USA Cornell Tech New York NY USA Weill Cornell Med New York NY USA

ISBN: (纸本)9781450394215

Clinical decision support tools (DSTs), powered by Artificial Intelligence (AI), promise to improve clinicians' diagnostic and treatment decision-making. However, no AI model is always correct. DSTs must enable clinicians to validate each AI suggestion, convincing them to take the correct suggestions while rejecting its errors. While prior work often tried to do so by explaining AI's inner workings or performance, we chose a different approach: We investigated how clinicians validated each other's suggestions in practice (often by referencing scientific literature) and designed a new DST that embraces these naturalistic interactions. This design uses GPT-3 to draw literature evidence that shows the AI suggestions' robustness and applicability (or the lack thereof). A prototyping study with clinicians from three disease areas proved this approach promising. Clinicians' interactions with the prototype also revealed new design and research opportunities around (1) harnessing the complementary strengths of literature-based and predictive decision supports;(2) mitigating risks of de-skilling clinicians;and (3) offering low-data decision support with literature.

关键词： Clinical AI XAI Biomedical Literature Qualitative Method

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Patterns of Effective human-Agent Teams

Patterns of Effective Human-Agent Teams

引用

Extended Abstracts of the 2023 chi conference on human factors in computing systems, chi EA 2023

作者： Momose, Kazuhiko Weekes, Troy Mehta, Rahul Wright, Cameron Moukpe, Josias Eskridge, Thomas Human-Centered Design L3Harris Institute for Assured Information Florida Institute of Technology MelbourneFL United States Computer Science L3Harris Institute for Assured Information Florida Institute of Technology MelbourneFL United States Computer Science Human-Centered Design L3Harris Institute for Assured Information Florida Institute of Technology MelbourneFL United States

ISBN: (纸本)9781450394222

Intelligent systems are increasingly interacting with people, both in their daily lives and through their use in safety critical systems. Current research is focused on how to use intelligent systems in a collaborative way as a teammate, rather than a tool. This requires a better understanding of what behaviors enable effective human-agent teams. This paper reports an experiment where a human player collaborates with an agent to perform a maneuvering task while concurrently performing a memory task. The player must determine in which contexts the agent requires their input to achieve better combined game plus memory task accuracy scores. We hypothesized that high-performing teams would exhibit different patterns of control inputs when compared to low-performing teams and that these patterns of control would be made more evident with user interfaces that increased operator situation awareness. Preliminary results are inconclusive, but show different patterns of interaction between high- and low-performing teams. © 2023 Owner/Author.

关键词： Intelligent systems

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Dying, Death, and the Afterlife in human-Computer Interaction. A Scoping Review. 23

Dying, Death, and the Afterlife in Human-Computer Interactio...

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2023 chi conference on human factors in computing systems, chi 2023

作者： Albers, Ruben Sadeghian, Shadan Laschke, Matthias Hassenzahl, Marc University of Siegen North Rhine-Westphalia Siegen Germany

ISBN: (纸本)9781450394215

Dying is a universal experience that entails uncertainty, loss, and termination. Often, people face death unprepared and miss out on opportunities to shape their final stage of life as well as their afterlife. To better understand how thanato-technology can support the dying and the bereaved, we performed a scoping review on the current state-of-art in human Computer Interaction. Following the PRISMA-ScR procedure, we gathered and analyzed 107 relevant papers. We categorized theoretical and conceptual contributions into three overarching themes: digital remains, remembrance, and coping. We further highlight 18 practices, such as curation, honoring and letting go. We show that technology can help to capture the identity of the deceased, to validate the life lived, and to come to terms with death. However, available approaches focus more on the bereaved than on the dying. In addition, potentially important aspects of dying (e.g., balancing involvement and autonomy, spiritual meaning-making) remain largely unexplored. © 2023 ACM.

关键词： human computer interaction

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Edu-larp @ chi

Edu-larp @ CHI

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Extended Abstracts of the 2023 chi conference on human factors in computing systems, chi EA 2023

作者： Robinson, Raquel B. Johansson, Karin Fey, James Collin Márquez Segura, Elena Back, Jon Waern, Annika Bowman, Sarah Lynne Isbister, Katherine Social Emotional Technology Lab University of California Santa Cruz United States Uppsala University Sweden Department of Computer Science and Engineering Universidad Carlos Iii de Madrid Madrid Spain

ISBN: (纸本)9781450394222

Role-play has long been used as an active learning technique in educational and training contexts. In particular, Edu-larp (a structured, live action roleplay experience that teaches through social enactment and reflection [1]) has been used in therapeutic contexts, training courses, and even an entire course curriculum [4]. We propose to host a workshop at chi 2023 which will connect chi attendees of various disciplines interested in this topic, with the outcome to understand how edu-larp might be an effective way of augmenting existing teaching and research within HCI. During the workshop, attendees will participate in numerous edu-larp exercises designed to introduce and orient them to the concept, and facilitate discussion about the different ways edu-larp can be leveraged in the broad domain of HCI. © 2023 Owner/Author.

关键词： human computer interaction

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Tackling Discrimination in Tech: The Anti-Bias Cards (ABC) in Use

Tackling Discrimination in Tech: The Anti-Bias Cards (ABC) i...

引用

Extended Abstracts of the 2023 chi conference on human factors in computing systems, chi EA 2023

作者： Burtscher, Sabrina Spiel, Katta Human Computer Interaction Group Tu Wien Vienna Austria

ISBN: (纸本)9781450394222

How to engage with the consequences of systemic biases and subsequent calls to ensure more equity in technologically oriented teams without any prior education on these matters presents an open question within human-Computer Interaction (HCI). Based on a previous literature study and the development of the Anti-Bias Card Deck (ABC), this paper presents the findings and insights from various workshops in which the cards were applied. The settings varied in their thematic and geographic contexts as well as with regard to participants and their backgrounds. Overall, participants agreed that the card deck could be of help to detect and tackle different aspects of discrimination over a project's life cycle. Hence, the ABC deck fulfilled its intended role as a potential conversation starter for reflecting on matters of bias and equity in teams of technology developers without prior expertise. © 2023 Owner/Author.

关键词： human computer interaction

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AI Literacy: Finding Common Threads between Education, Design, Policy, and Explainability

AI Literacy: Finding Common Threads between Education, Desig...

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Extended Abstracts of the 2023 chi conference on human factors in computing systems, chi EA 2023

作者： Long, Duri Roberts, Jessica Magerko, Brian Holstein, Kenneth Dipaola, Daniella Martin, Fred Department of Communication Studies Northwestern University United States School of Interactive Computing Georgia Tech United States School of Literature Media and Communication Georgia Tech United States Human-Computer Interaction Institute Carnegie Mellon University United States Personal Robots Group Mit Media Lab United States Computer Science University of Massachusetts Lowell United States

ISBN: (纸本)9781450394222

Fostering public AI literacy has been a growing area of interest at chi for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy. © 2023 Owner/Author.

关键词： Public policy

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Understanding Choice Independence and Error Types in human-AI Collaboration 24

Understanding Choice Independence and Error Types in Human-A...

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chi conference on human factors in computing Sytems (chi)

作者： Erlei, Alexander Sharma, Abhinav Gadiraju, Ujwal Georg August Univ Gottingen Gottingen Germany IIIT Guwahati Gauhati India Delft Univ Technol Delft Netherlands

ISBN: (纸本)9798400703300

The ability to make appropriate delegation decisions is an important prerequisite of effective human-AI collaboration. Recent work, however, has shown that people struggle to evaluate AI systems in the presence of forecasting errors, falling well short of relying on AI systems appropriately. We use a pre-registered crowdsourcing study (N = 611) to extend this literature by two underexplored crucial features of human AI decision-making: choice independence and error type. Subjects in our study repeatedly complete two prediction tasks and choose which predictions they want to delegate to an AI system. For one task, subjects receive a decision heuristic that allows them to make informed and relatively accurate predictions. The second task is substantially harder to solve, and subjects must come up with their own decision rule. We systematically vary the AI system's performance such that it either provides the best possible prediction for both tasks or only for one of the two. Our results demonstrate that people systematically violate choice independence by taking the AI's performance in an unrelated second task into account. humans who delegate predictions to a superior AI in their own expertise domain significantly reduce appropriate reliance when the model makes systematic errors in a complementary expertise domain. In contrast, humans who delegate predictions to a superior AI in a complementary expertise domain significantly increase appropriate reliance when the model systematically errs in the human expertise domain. Furthermore, we show that humans differentiate between error types and that this effect is conditional on the considered expertise domain. This is the first empirical exploration of choice independence and error types in the context of human-AI collaboration. Our results have broad and important implications for the future design, deployment, and appropriate application of AI systems.

关键词： human-AI Collaboration Interaction Algorithm Aversion Errors Complementary AI systems Decision Support System Crowdsourcing Study

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GustosonicSense: Towards understanding the design of playful gustosonic eating experiences 24

GustosonicSense: Towards understanding the design of playful...

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chi conference on human factors in computing Sytems (chi)

作者： Wang, Yan Obie, Humphrey O. Li, Zhuying Salim, Flora D. Grundy, John Mueller, Florian ' Floyd' Univ Melbourne Exert Game Lab Dept Human Centred Comp Melbourne Vic Australia Swinburne Univ Technol Dept Comp Technol Melbourne Vic Australia Monash Univ Melbourne HumaniSE Lab Dept Software Syst & Cybersecur Victoria Australia Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Univ New South Wales UNSW Sydney Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9798400703300

The pleasure that often comes with eating can be further enhanced with intelligent technology, as the field of human-food interaction suggests. However, knowledge on how to design such pleasure-supporting eating systems is limited. To begin filling this knowledge gap, we designed "GustosonicSense", a novel gustosonic eating system that utilizes wireless earbuds for sensing different eating and drinking actions with a machine learning algorithm and trigger playful sounds as a way to facilitate pleasurable eating experiences. We present the findings from our design and a study that revealed how we can support the "stimulation", "hedonism", and "reflexivity" for playful human-food interactions. Ultimately, with our work, we aim to support interaction designers in facilitating playful experiences with food.

关键词： human-food interaction gustosonic experiences play eating earbuds

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I Know Your Feelings Before You Do: Predicting Future Affective Reactions in human-Computer Dialogue

I Know Your Feelings Before You Do: Predicting Future Affect...

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Extended Abstracts of the 2023 chi conference on human factors in computing systems, chi EA 2023

作者： Li, Yuanchao Inoue, Koji Tian, Leimin Fu, Changzeng Ishi, Carlos Toshinori Ishiguro, Hiroshi Kawahara, Tatsuya Lai, Catherine University of Edinburgh United Kingdom Kyoto University Japan Monash University Australia Osaka University Riken Japan Atr Riken Japan Osaka University Atr Japan

ISBN: (纸本)9781450394222

Current Spoken Dialogue systems (SDSs) often serve as passive listeners that respond only after receiving user speech. To achieve human-like dialogue, we propose a novel future prediction architecture that allows an SDS to anticipate future affective reactions based on its current behaviors before the user speaks. In this work, we investigate two scenarios: speech and laughter. In speech, we propose to predict the user's future emotion based on its temporal relationship with the system's current emotion and its causal relationship with the system's current Dialogue Act (DA). In laughter, we propose to predict the occurrence and type of the user's laughter using the system's laughter behaviors in the current turn. Preliminary analysis of human-robot dialogue demonstrated synchronicity in the emotions and laughter displayed by the human and robot, as well as DA-emotion causality in their dialogue. This verifies that our architecture can contribute to the development of an anticipatory SDS. © 2023 Owner/Author.

关键词： Forecasting

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