Abstract
In time-critical detection tasks, such as drone monitoring, a key condition for users to effectively leverage AI assistance is to find an appropriate trade-off between making fast decisions and verifying AI suggestions, which we refer to as appropriate user reliance. However, assessing such reliance is often oversimplified by focusing solely on task outcomes, potentially overlooking whether users properly verify AI messages. We collected eye-tracking data from an AI-assisted monitoring task and developed a gaze-based reliance model: RelEYEance, to assess the extent of user reliance on AI-suggested alarms. We found that gaze patterns related to verification behaviors distinguish between appropriate reliance, over-reliance, and under-reliance, influencing task performance. We validated our model in a second user study, showing it can reliably detect users’ over- and under-reliance at run-time, which could be used e.g. for issuing intervention messages. The results demonstrate the potential for real-time human-AI reliance assessment, facilitating adaptive reliance calibration.
Code and Data: https://osf.io/8r5pt
Presentation: https://gamma.app/docs/ETRA2025-kini7wzqakx0494?mode=doc
Paper: https://doi.org/10.1145/3725841
BibTeX (Download)
@article{WuETRA2025,
title = {RelEYEance: Gaze-Based Assessment of Users’ AI-reliance at Run-Time},
author = {Zekun Wu and Yao Wang and Markus Langer and Anna Maria Feit},
url = {https://doi.org/10.1145/3725841},
doi = {10.1145.3725841},
year = {2025},
date = {2025-05-23},
urldate = {2025-05-23},
journal = {Proc. ACM Hum.-Comput. Interact.},
volume = {9},
number = {ETRA16},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {In time-critical detection tasks, such as drone monitoring, a key condition for users to effectively leverage AI assistance is to find an appropriate trade-off between making fast decisions and verifying AI suggestions, which we refer to as appropriate user reliance. However, assessing such reliance is often oversimplified by focusing solely on task outcomes, potentially overlooking whether users properly verify AI messages. We collected eye-tracking data from an AI-assisted monitoring task and developed a gaze-based reliance model: RelEYEance, to assess the extent of user reliance on AI-suggested alarms. We found that gaze patterns related to verification behaviors distinguish between appropriate reliance, over-reliance, and under-reliance, influencing task performance. We validated our model in a second user study, showing it can reliably detect users’ over- and under-reliance at run-time, which could be used e.g. for issuing intervention messages. The results demonstrate the potential for real-time human-AI reliance assessment, facilitating adaptive reliance calibration.},
keywords = {CPEC, eye tracking, Human oversight, Human-AI interaction},
pubstate = {published},
tppubtype = {article}
}