EXPLAINABLE AI IN DATA ANALYTICS: ENHANCING TRANSPARENCY AND TRUST IN COMPLEX MACHINE LEARNING MODELS

Abstract

This article provides a comprehensive exploration of Explainable AI (XAI) and its critical role in enhancing transparency and interpretability in data analytics, particularly for complex machine learning models. We begin by examining the theoretical framework of XAI, including its definition, importance in machine learning, and regulatory considerations in sectors such as healthcare and finance. The article then delves into key XAI concepts, including feature importance, surrogate models, Local Interpretable Model-agnostic Explanations (LIME), and SHapley Additive exPlanations (SHAP). A detailed case study on implementing an XAI framework for credit scoring models demonstrates the practical application of these techniques, highlighting their potential to improve model transparency and build trust among stakeholders. The article addresses the benefits of XAI in data analytics, current limitations and challenges, ethical considerations, and future research directions. By synthesizing current research and providing practical insights, this article contributes to the ongoing dialogue on responsible AI development and deployment, emphasizing the crucial role of explainability in fostering trust, ensuring fairness, and meeting regulatory requirements in an increasingly AI-driven world.