An extensive review on significance of Explainable Artificial Intelligence models in discrete domains for informed decisions making

Machine learning approaches and models subset of Artificial Intelligence are becoming increasingly complex and incomprehensible.While domain specialists grasp the mathematical theory, they have difficulty communicating the reasoning to a broad audience.To solve this challenge, a new research topic called Explainable Artificial Intelligence (XAI) has arisen to develop a contextual explanatory model for practical deployment.Explainable Artificial Intelligence aims to make AI models interpretable and transparent by providing human-understandable explanations for their decisions.Techniques like feature visualization and attribution methods offer insights into AI decision-making, benefiting healthcare, finance, and autonomous vehicles applications.XAI enhances trust, accountability, and fairness in AI systems by allowing users to comprehend the reasoning behind predictions.However, striking a balance between interpretability and performance is challenging.Achieving this balance is crucial to leverage the potential of XAI in building trustworthy and ethical AI across various domains.This essential taxonomy describes the prospects and problems in the field of XAI and serves as a resource for future AI researchers.The study authors examined XAI's role in simplifying machine learning models, providing understandable explanations for AI decisions, promoting trust and accountability, and optimizing performance across diverse applications of military, healthcare and communications.