Bridging the Gap: Enhancing Trust and Transparency in Machine Learning with Explainable AI

Abstract

Explainable Artificial Intelligence (XAI) aims to address the complexity and opacity of AI systems, often referred to as "black boxes." It seeks to provide transparency and build trust in AI, particularly in domains where decisions impact safety, security, and ethical considerations. XAI approaches fall into three categories: opaque systems that offer no explanation for their predictions, interpretable systems that provide some level of justification, and comprehensible systems that enable users to reason about and interact with the AI system. Automated reasoning plays a crucial role in achieving truly explainable AI. This paper presents current methodologies, challenges, and the importance of integrating automated reasoning for XAI. It is grounded in a thorough literature review and case studies, providing insights into the practical applications and future directions for XAI.