2022/06/29 by Subash Khanal, Benjamin Collins Brodie, Khanal, Subash +6
Computer Science · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2206.14841
openalex publication_date 2022/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
There have been several post-hoc explanation approaches developed to explain pre-trained black-box neural networks. However, there is still a gap in research efforts toward designing neural networks that are inherently explainable. In this paper, we utilize a recently proposed instance-wise post-hoc causal explanation method to make an existing transformer architecture inherently explainable. Once trained, our model provides an explanation in the form of top-k regions in the input space of the given instance contributing to its decision. We evaluate our method on binary classification tasks using three image datasets: MNIST, FMNIST, and CIFAR. Our results demonstrate that compared to the causality-based post-hoc explainer model, our inherently explainable model achieves better explainability results while eliminating the need of training a separate explainer model. Our code is available at https://github.com/mvrl/CAT-XPLAIN.