  This paper proposes a self-explainable Deep Learning (SE-DL) system for an
image classification problem that performs self-error detection. The self-error
detection is key to improving the DL system's safe operation, especially in
safety-critical applications such as automotive systems. A SE-DL system outputs
both the class prediction and an explanation for that prediction, which
provides insight into how the system makes its predictions for humans.
Additionally, we leverage the explanation of the proposed SE-DL system to
detect potential class prediction errors of the system. The proposed SE-DL
system uses a set of concepts to generate the explanation. The concepts are
human-understandable lower-level image features in each input image relevant to
the higher-level class of that image. We present a concept selection
methodology for scoring all concepts and selecting a subset of them based on
their contribution to the error detection performance of the proposed SE-DL
system. Finally, we present different error detection schemes using the
proposed SE-DL system to compare them against an error detection scheme without
any SE-DL system.
