Explainable AI for Computer Vision

Abstract

Most machine learning and deep learning models lack a way of explaining and interpreting results. Due to the dynamic nature of deep learning models and increasing state-of-the-art models, the current model evaluation is based on accuracy scores. This makes machine learning and deep learning black-box models. This leads to lack of confidence in applying the model and lack of trust of the generated results. There are multiple libraries that help us explain models of structured data like SHAP and LIME. This chapter explains computer vision model outputs.