Understanding User Reliance on AI in Assisted Decision-Making
Understanding User Reliance on AI in Assisted Decision-Making
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DOI：
10.1145/3555572
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发表时间：
2022-11
影响因子：
--
通讯作者：
Shiye Cao;Chien-Ming Huang
中科院分区：
文献类型：
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作者：
Shiye Cao;Chien-Ming Huang
正确校准人类对人工智能的依赖是在人工智能辅助人类决策中实现互补性能的基础。以前的大多数工作都侧重于通过用户感知和基于任务的措施来回顾性地评估用户的依赖度和更广泛的信任。在这项工作中，我们探讨了空间推理任务中不同任务难度和人工智能表现水平下眼睛注视和依赖之间的关系。我们的结果表明，人工智能建议的注视持续时间百分比与用户人工智能任务一致性以及用户感知的依赖度之间存在很强的正相关性。此外，特别是当任务很简单以及人工智能性能较低或不一致时，用户代理得以保留。我们的结果还揭示了依赖和信任之间的细微差别。我们讨论了使用眼睛注视来实时评估人类对人工智能的依赖程度的潜力，从而实现自适应人工智能辅助，以实现最佳的人类人工智能团队绩效。
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.