Meta-XAI for Explaining the Explainer: Unveiling Image Features Driving Deep Learning Decisions

Abstract

Deep learning has revolutionized computer vision by allowing neural networks to automatically learn features from data. However, the highly nonlinear nature of deep neural networks makes them difficult to interpret, leading to concerns about potential biases in critical applications. To address this, researchers have advocated for explainable artificial intelligence (XAI). Many XAI techniques have been proposed but all of them only highlight image regions influencing model decisions, lacking any further explanations. In this article, we propose a posthoc model-agnostic meta-XAI method that explains why specific image regions are used for decisions. The article presents the experimental setup and results, discussing the perturbations used for explanations in color, frequency, shape, shading, and texture. The explanation is given in terms of human-interpretable image features, e.g., color, shape, shading, and texture both as perturbation plots and as visual summary through the use of the newly introduced normalized area under the curve score. The experimental results confirm the previous findings that vision deep learning models are biased toward texture, but also highlight the importance of color, frequency content, and perceptually salient structures in the final decision.