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Title: Understanding User Reliance on AI in Assisted Decision-Making
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.  more » « less
Award ID(s):
1840088
PAR ID:
10410672
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the ACM on Human-Computer Interaction
Volume:
6
Issue:
CSCW2
ISSN:
2573-0142
Page Range / eLocation ID:
1 to 23
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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