Explainable AI (XAI) & Model Interpretability

Abstract

Artificial Intelligence (AI) systems are increasingly used in critical applications such as healthcare, finance, education, and governance. However, many modern AI models, especially deep learning networks, operate as “black boxes” where decision making process is not easily understood by humans. This lack of transparency leads to problems of trust, accountability, fairness, and regulatory compliance. Explainable AI (XAI) has emerged as an important research area to make AI systems more transparent, interpretable, and trustworthy. This paper presents a comprehensive review of Explainable AI techniques and model interpretability approaches. Various methods including intrinsic interpretability, post-hoc explanations, feature importance, visualization tools, and rule-based explanations are discussed. The paper also highlights the importance of XAI in real world applications and current challenges in the field. Tables and figures are provided for better understanding of different techniques. Finally, future directions of research in XAI are discussed.

KEYWORDS: Explainable AI, Model Interpretability, Black Box Models, Trustworthy AI, Feature Importance, Post-hoc Explanation, Transparency