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Understanding User Reliance on AI in Assisted Decision-Making

Authors:

Chien-Ming HuangAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 6, Issue CSCW2

Article No.: 471, Pages 1 - 23

https://doi.org/10.1145/3555572

Published: 11 November 2022 Publication History

Abstract

Proper calibration of human reliance on AI is fundamental to achieving complementary performance in AI-assisted human decision-making. Most previous works focused on assessing user reliance, and more broadly trust, retrospectively, through user perceptions and task-based measures. In this work, we explore the relationship between eye gaze and reliance under varying task difficulties and AI performance levels in a spatial reasoning task. Our results show a strong positive correlation between percent gaze duration on the AI suggestion and user AI task agreement, as well as user perceived reliance. Moreover, user agency is preserved particularly when the task is easy and when AI performance is low or inconsistent. Our results also reveal nuanced differences between reliance and trust. We discuss the potential of using eye gaze to gauge human reliance on AI in real-time, enabling adaptive AI assistance for optimal human-AI team performance.

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Kurtz GAmzalag MShaked NZaguri YKohen-Vacs DGal EZailer GBarak-Medina E(2024)Strategies for Integrating Generative AI into Higher Education: Navigating Challenges and Leveraging OpportunitiesEducation Sciences10.3390/educsci1405050314:5(503)Online publication date: 7-May-2024
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Index Terms

Understanding User Reliance on AI in Assisted Decision-Making
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 6, Issue CSCW2

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November 2022

8205 pages

EISSN:2573-0142

DOI:10.1145/3571154

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Published: 11 November 2022

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https://doi.org/10.3390/educsci14050503
Mei LLiu RYin ZZhao QJiang WWang SWang SLu KHe T(2024)mmSpyVR: Exploiting mmWave Radar for Penetrating Obstacles to Uncover Privacy Vulnerability of Virtual RealityProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997728:4(1-29)Online publication date: 21-Nov-2024
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Bian HGuo BLiu SDing YGao SYu Z(2024)UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous DevicesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997718:4(1-26)Online publication date: 21-Nov-2024
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