An Approach to AI That Is Both Accurate and Easy to Understand, Called Explainable AI (XAI)

Abstract

An important area of research known as Explainable AI (XAI) has emerged in response to the growing need for trustworthy, accountable, and transparent intelligent machine learning systems. Despite advanced models, particularly deep learning architectures, achieving remarkable accuracy in domains such as healthcare, autonomous systems, and finance, their interpretability is often limited and they are not applied in high-stakes decision-making situations due to their "black-box" nature. Finding a happy medium between model openness and prediction accuracy is a big challenge for academics and industry professionals. In an effort to bridge this gap, XAI is developing accurate but accessible methods of explaining model behavior to humans. Researchers have used tools such as intrinsically interpretable models, model-agnostic explanation methods (such as LIME and SHAP), attention processes, and feature importance analysis to better understand the prediction generation process. The employment of these approaches allows for the validation of model judgments, the detection of biases, and the assurance of regulatory and ethical compliance.