CAVLI - Using Image Associations to Produce Local Concept-Based Explanations

Abstract

While explainability is becoming increasingly crucial in computer vision and machine learning, producing explanations that can link decisions made by deep neural networks to concepts that are easily understood by humans still remains a challenge. To address this challenge, we propose a framework that produces local concept-based explanations for the classification decisions made by a deep neural network. Our framework is based on the intuition that if there is a high overlap between the regions of the image that are associated with a human-defined concept and regions of the image that are useful for decision-making, then the decision is highly dependent on the concept. Our proposed CAVLI framework combines a global approach (TCAV) with a local approach (LIME). To test the effectiveness of the approach, we conducted experiments on both the ImageNet and CelebA datasets. These experiments validate the ability of our framework to quantify the dependence of individual decisions on predefined concepts. By providing local concept-based explanations, our framework has the potential to improve the transparency and interpretability of deep neural networks in a variety of applications.