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From Human-Human Collaboration to Human-AI Collaboration: Designing AI Systems That Can Work Together with People

Published: 25 April 2020 Publication History

Abstract

Artificial Intelligent (AI) and Machine Learning (ML) algorithms are coming out of research labs into the real-world applications, and recent research has focused a lot on Human-AI Interaction (HAI) and Explainable AI (XAI). However, Interaction is not the same as Collaboration. Collaboration involves mutual goal understanding, preemptive task co-management and shared progress tracking. Most of human activities today are done collaboratively, thus, to integrate AI into the already-complicated human workflow, it is critical to bring the Computer-Supported Cooperative Work (CSCW) perspective into the root of the algorithmic research and plan for a Human-AI Collaboration future of work. In this panel we ask: Can this future for trusted human-AI collaboration be realized? If so, what will it take? This panel will bring together HCI experts who work on human collaboration and AI applications in various application contexts, from industry and academia and from both the U.S. and China. Panelists will engage the audience through discussion of their shared and diverging visions, and through suggestions for opportunities and challenges for the future of human-AI collaboration.

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Robert Lindsey, Aaron Daluiski, Sumit Chopra, et al. 2018. Deep neural network improves fracture detection by clinicians. Proceedings of the National Academy of Sciences 115, 45: 11591--11596.
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Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. "Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1--24.
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Jaimie Drozdal, Justin Weisz, Dakuo Wang, Gaurav Dass, Bingsheng Yao, Changruo Zhao, Michael Muller, Lin Ju, and Hui Su. "Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems." arXiv preprint arXiv:2001.06509 (2020).
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    cover image ACM Conferences
    CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems
    April 2020
    4474 pages
    ISBN:9781450368193
    DOI:10.1145/3334480
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Publication History

    Published: 25 April 2020

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    Author Tags

    1. ai partner
    2. ai-powered healthcare
    3. computer-supported corporative work
    4. explainable ai
    5. group collaboration
    6. human-ai collaboration
    7. trusted ai

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    Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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    CHI 2025
    ACM CHI Conference on Human Factors in Computing Systems
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    • (2025)Enhancing Cruise Booking Systems: Integrating Human Expertise with AI through Agile MethodsInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT2511127411:1(686-694)Online publication date: 13-Jan-2025
    • (2025)Promises and Pitfalls: Using Large Language Models to Generate Visualization ItemsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345630931:1(1094-1104)Online publication date: 1-Jan-2025
    • (2025)Trust Your Gut: Comparing Human and Machine Inference from Noisy VisualizationsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345618231:1(754-764)Online publication date: 1-Jan-2025
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