Explainable AI: Enhancing Transparency and Trust in Artificial Intelligence Systems

Abstract

The rapid advancement of artificial intelligence (AI) has led to its widespread adoption across various domains. However, the complexity and opacity of AI models, particularly deep learning systems, have raised concerns about their transparency and trustworthiness. This study explores the development and implementation of Explainable AI (XAI) techniques aimed at making AI systems more interpretable and understandable to human users. By employing methods such as model-agnostic explanations, visualization tools, and self-explaining models, we demonstrate how XAI can enhance user trust and facilitate better decision-making in AI-driven applications. Our findings indicate that integrating XAI techniques not only improves transparency but also increases the overall reliability and acceptance of AI systems.