Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network
This work addresses the problem of interpretability in AI for users needing transparent decision-making, but it appears incremental as it builds on prior error correction methods.
The study tackles the lack of transparency in deep neural networks by generalizing a layerwise error correction method to improve interpretability heatmaps, assuming groundtruth interpretable information exists, and proposes a tailored filtering technique for signal rectification.
The practical application of deep neural networks are still limited by their lack of transparency. One of the efforts to provide explanation for decisions made by artificial intelligence (AI) is the use of saliency or heat maps highlighting relevant regions that contribute significantly to its prediction. A layer-wise amplitude filtering method was previously introduced to improve the quality of heatmaps, performing error corrections by noise-spike suppression. In this study, we generalize the layerwise error correction by considering any identifiable error and assuming there exists a groundtruth interpretable information. The forms of errors propagated through layerwise relevance methods are studied and we propose a filtering technique for interpretability signal rectification taylored to the trend of signal amplitude of the particular neural network used. Finally, we put forth arguments for the use of groundtruth interpretable information.