Book Title: Intelligent Systems
Editors: Dr. S.C. Vettivel, Dr. Leema Nelson and Dr. D. Poornima
ISBN: 978-81-979197-4-9
Chapter: 3
DOI: https://doi.org/10.59646/isc3/259
Author: R. Radhika, Assistant Professor, Department of AI & DS, RVS College of Engineering and Technology, Kumaran Kottam Campus, Kannampalayam, Sulur, Coimbatore, Tamil Nadu, India.
Abstract
Explainable AI (XAI) is emerging as a crucial component in the deployment of artificial intelligence systems, addressing the need for transparency, trust, and ethical considerations in AI decision-making processes. This chapter delves into the importance of XAI by exploring its role in enhancing trust and transparency, improving AI adoption across industries, and ensuring compliance with ethical and legal standards. The challenges in achieving explainability, particularly in complex AI models like deep learning, are examined in detail, highlighting the trade-offs between accuracy and interpretability. Additionally, the chapter discusses the various techniques for achieving explainability, including post-hoc methods, inherently interpretable models, and the use of visualization tools. The ethical and social implications of XAI are explored, emphasizing its role in detecting and mitigating bias, empowering users, and aligning with regulatory frameworks. Finally, the chapter outlines future directions in XAI, focusing on advancing bias detection techniques, enhancing user-centric explainability, and integrating XAI into broader AI governance frameworks.