  Explainable artificial intelligence (XAI) aims to develop transparent
explanatory approaches for "black-box" deep learning models. However,it remains
difficult for existing methods to achieve the trade-off of the three key
criteria in interpretability, namely, reliability, causality, and usability,
which hinder their practical applications. In this paper, we propose a
self-supervised automatic semantic interpretable explainable artificial
intelligence (AS-XAI) framework, which utilizes transparent orthogonal
embedding semantic extraction spaces and row-centered principal component
analysis (PCA) for global semantic interpretation of model decisions in the
absence of human interference, without additional computational costs. In
addition, the invariance of filter feature high-rank decomposition is used to
evaluate model sensitivity to different semantic concepts. Extensive
experiments demonstrate that robust and orthogonal semantic spaces can be
automatically extracted by AS-XAI, providing more effective global
interpretability for convolutional neural networks (CNNs) and generating
human-comprehensible explanations. The proposed approach offers broad
fine-grained extensible practical applications, including shared semantic
interpretation under out-of-distribution (OOD) categories, auxiliary
explanations for species that are challenging to distinguish, and
classification explanations from various perspectives.
