  Deep learning models are being increasingly applied to imbalanced data in
high stakes fields such as medicine, autonomous driving, and intelligence
analysis. Imbalanced data compounds the black-box nature of deep networks
because the relationships between classes may be highly skewed and unclear.
This can reduce trust by model users and hamper the progress of developers of
imbalanced learning algorithms. Existing methods that investigate imbalanced
data complexity are geared toward binary classification, shallow learning
models and low dimensional data. In addition, current eXplainable Artificial
Intelligence (XAI) techniques mainly focus on converting opaque deep learning
models into simpler models (e.g., decision trees) or mapping predictions for
specific instances to inputs, instead of examining global data properties and
complexities. Therefore, there is a need for a framework that is tailored to
modern deep networks, that incorporates large, high dimensional, multi-class
datasets, and uncovers data complexities commonly found in imbalanced data
(e.g., class overlap, sub-concepts, and outlier instances). We propose a set of
techniques that can be used by both deep learning model users to identify,
visualize and understand class prototypes, sub-concepts and outlier instances;
and by imbalanced learning algorithm developers to detect features and class
exemplars that are key to model performance. Our framework also identifies
instances that reside on the border of class decision boundaries, which can
carry highly discriminative information. Unlike many existing XAI techniques
which map model decisions to gray-scale pixel locations, we use saliency
through back-propagation to identify and aggregate image color bands across
entire classes. Our framework is publicly available at
\url{https://github.com/dd1github/XAI_for_Imbalanced_Learning}
