  This paper investigates the use of Mean Opinion Score (MOS), a common image
quality metric, as a user-centric evaluation metric for XAI post-hoc
explainers. To measure the MOS, a user experiment is proposed, which has been
conducted with explanation maps of intentionally distorted images. Three
methods from the family of feature attribution methods - Gradient-weighted
Class Activation Mapping (Grad-CAM), Multi-Layered Feature Explanation Method
(MLFEM), and Feature Explanation Method (FEM) - are compared with this metric.
Additionally, the correlation of this new user-centric metric with automatic
metrics is studied via Spearman's rank correlation coefficient. MOS of MLFEM
shows the highest correlation with automatic metrics of Insertion Area Under
Curve (IAUC) and Deletion Area Under Curve (DAUC). However, the overall
correlations are limited, which highlights the lack of consensus between
automatic and user-centric metrics.
