In-Depth Hyperparameter Selection For Layer-Wise Relevance Propagation

Master's Thesis at TU Berlin's ML/IDA Group headed by Prof. Dr. Klaus-Robert Müller.

Abstract.
Our expectations of Explainable AI have grown together with its popularity. So far, the interpretability technique of Layer-Wise Relevance Propagation (LRP) has been adopted with mostly qualitative evaluation of its rules. Therefore, a quantitative and qualitative evaluation of LRP rules is conducted to determine which hyperparameters provide the best scoring heatmaps according to the Pixel-Flipping and Area Under the Curve evaluation framework. It can be concluded from the experiment results that the choice of evaluation metrics and visualization of heatmaps has a significant impact on explanations. Additionally, due to the inherent subjectivity of visual explanations the requirements should be defined on a case-by-case basis.