EXPLAINABLE AI: DEMYSTIFYING THE BLACK BOX OF MACHINE LEARNING

Abstract

The burgeoning field of Artificial Intelligence (AI) has revolutionized diverse domains, from healthcare and finance to transportation and entertainment. However, the "black box" nature of many machine learning models, where internal decision-making processes remain opaque, raises concerns about bias, fairness, and accountability. Explainable AI (XAI) emerges as a critical response to this challenge, aiming to provide transparency and interpretability into how models arrive at their outputs. This article delves into the conceptual landscape of XAI, exploring its motivations, approaches, and potential applications. We discuss diverse XAI methods, ranging from white-box models and rule-based systems to post-hoc interpretability techniques like feature importance analysis and counterfactual explanations. Further, we examine the ethical and societal implications of XAI, considering its role in mitigating bias, ensuring fairness, and building trust in AI systems. Finally, we highlight promising research directions and challenges in XAI, emphasizing the need for continued development towards truly human-interpretable AI.