Authors: Dr. V. Ramesh, Ms. Divya M.
Abstract: Explainable Artificial Intelligence (XAI) addresses the growing need for transparency in AI decision-making systems. As AI systems are increasingly deployed in critical domains such as healthcare, finance, and legal sectors, understanding the rationale behind their decisions becomes imperative. This paper explores recent developments in interpretable models, post-hoc explanation techniques, and evaluation methods. It emphasizes the balance between model accuracy and interpretability and provides real-world examples showcasing the necessity of XAI in high-stakes applications.
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