  Gaze-based interactions offer a potential way for users to naturally engage
with mixed reality (XR) interfaces. Black-box machine learning models enabled
higher accuracy for gaze-based interactions. However, due to the black-box
nature of the model, users might not be able to understand and effectively
adapt their gaze behaviour to achieve high quality interaction. We posit that
explainable AI (XAI) techniques can facilitate understanding of and interaction
with gaze-based model-driven system in XR. To study this, we built a real-time,
multi-level XAI interface for gaze-based interaction using a deep learning
model, and evaluated it during a visual search task in XR. A between-subjects
study revealed that participants who interacted with XAI made more accurate
selections compared to those who did not use the XAI system (i.e., F1 score
increase of 10.8%). Additionally, participants who used the XAI system adapted
their gaze behavior over time to make more effective selections. These findings
suggest that XAI can potentially be used to assist users in more effective
collaboration with model-driven interactions in XR.
