Explaining Explainability: Towards Deeper Actionable Insights into Deep
  Learning through Second-order Explainability
E. Zhixuan Zeng Hayden Gunraj Sheldon Fernandez Alexander Wong
Explainability plays a crucial role in providing a more comprehensive
understanding of deep learning models' behaviour. This allows for thorough
validation of the model's performance, ensuring that its decisions are based on
relevant visual indicators and not biased toward irrelevant patterns existing
in training data. However, existing methods provide only instance-level
explainability, which requires manual analysis of each sample. Such manual
review is time-consuming and prone to human biases. To address this issue, the
concept of second-order explainable AI (SOXAI) was recently proposed to extend
explainable AI (XAI) from the instance level to the dataset level. SOXAI
automates the analysis of the connections between quantitative explanations and
dataset biases by identifying prevalent concepts. In this work, we explore the
use of this higher-level interpretation of a deep neural network's behaviour to
allows us to "explain the explainability" for actionable insights.
Specifically, we demonstrate for the first time, via example classification and
segmentation cases, that eliminating irrelevant concepts from the training set
based on actionable insights from SOXAI can enhance a model's performance.