An Explainable Artificial Intelligence (XAI) Framework for Deep Learning Based Classification to Generate Textual Explanations on Predicted Images

Abstract

The Explainable Artificial Intelligence (XAI) is a set of techniques and methods designed to make machine learning models and AI systems more transparent and interpretable. The goal of XAI is to enable humans to understand and trust the decisions made by AI systems, especially in critical or sensitive applications. In the context of generating explanations, XAI aims to provide human-understandable reasons for making a particular prediction or decision based on Artificial Intelligence (AI) model. XAI is an evolving field where the researchers have different ideas. Shapley values, which help explain the predictions, can be slow in calculation because they need to be figured out in many ways for each prediction. The current techniques for explaining AI are not fast and can be costly. This means they might not work well when trying to explain a lot of predictions at once. To overcome these issues, there developed a novel Convolution Bat Optimization based SHapley Additive exPlanations (CBO-SHAP) algorithm. Initially, a dataset contains a large number of images that are collected and pre-processed, then the data got divided into training, testing sets, and validation. Bat optimization is used for the classification and segmentation of the images which is performed in the pooling layer. To create the textual representation during testing, the trained images were applied to XAI with SHAP algorithm. Later, the image explanations were translated into the textual explanation that is readable by humans. This process enhances the whole operation with very less execution time of 78.3427sec. Comparative analyses against existing methods confirm the superior performance of our model, boasting high accuracy, f-measure, recall, and precision rates of approximately 99%, 99.28%, 99.22%, and 99.1% respectively.