Chapter 10 - Explainable AI: Techniques and Tools for Interpreting Deep Learning Models - Submission San International Scientific Publicaion

Chapter 10 – Explainable AI: Techniques and Tools for Interpreting Deep Learning Models

Haewon, Byeon¹ and S.Berlin Shaheema²
¹Department of Digital Anti-aging Healthcare (Bk21), Inje University, Republic of Korea.
²Associate Professor, Department of Artificial Intelligence and Data Science, Annai Vailankanni College of Engineering, Kanyakumari, India

Abstract
Explainable AI (XAI) refers to the development of artificial intelligence models and algorithms that can be understood and explained by humans. The goal of XAI is to create AI systems that not only produce accurate results but also provide insights into their decision-making process. In recent years, the use of machine learning and other AI technologies has become increasingly prevalent in a variety of fields, including healthcare, finance, and transportation. However, many AI models are often regarded as “black boxes,” meaning that the internal workings of the model are not transparent, and it is difficult for humans to understand how the model arrived at a particular decision.