Paper Contents
Abstract
In recent years, artificial intelligence (AI) has become a cornerstone of technological advancement, making strides in industries such as healthcare, finance, and autonomous driving. However, as AI systems, particularly deep learning models, grow in complexity, a critical challenge has emerged: explainability. Machine learning models, while powerful, are often viewed as black boxes, offering little insight into how decisions are made. This opacity raises concerns about trust, accountability, and ethical implications. As a result, the field of Explainable AI (XAI) has gained significant attention in both academia and industry. Explainable AI seeks to provide transparency into how AI models function and arrive at their conclusions, thereby enabling human users to understand and trust machine-driven decisions. This paper explores the evolution of XAI, its importance, key techniques, applications, and the ongoing challenges in the field.
Copyright
Copyright © 2025 Latika Kharb. This is an open access article distributed under the Creative Commons Attribution License.
