Applications of Explainable AI

This review paper explores the pivotal role of Explainable AI (XAI) within the domain of Artificial intelligence using machine learning and deep learning models. As deep learning is used more and more across various applications, its black-box nature often limits interpretability and trust in these models. XAI emerges as a crucial choice for addressing this challenge by elucidating complex neural networks' decision-making processes. This paper surveys recent advancements, methodologies, and applications of XAI techniques used for deep learning models. Highlighting the significance of transparent and interpretable AI, this review emphasizes how XAI not only enhances model trustworthiness but also empowers users and stakeholders with comprehensible insights, fostering widespread adoption and ethical deployment of deep learning technologies.