  As machine learning approaches are increasingly used to augment human
decision-making, eXplainable Artificial Intelligence (XAI) research has
explored methods for communicating system behavior to humans. However, these
approaches often fail to account for the emotional responses of humans as they
interact with explanations. Facial affect analysis, which examines human facial
expressions of emotions, is one promising lens for understanding how users
engage with explanations. Therefore, in this work, we aim to (1) identify which
facial affect features are pronounced when people interact with XAI interfaces,
and (2) develop a multitask feature embedding for linking facial affect signals
with participants' use of explanations. Our analyses and results show that the
occurrence and values of facial AU1 and AU4, and Arousal are heightened when
participants fail to use explanations effectively. This suggests that facial
affect analysis should be incorporated into XAI to personalize explanations to
individuals' interaction styles and to adapt explanations based on the
difficulty of the task performed.
