Explainable Artificial Intelligence for Reliable Decision-Making in Machine Learning Systems

Abstract

The increasing use of machine learning in critical applications has created a growing demand for transparent and interpretable artificial intelligence systems. Although complex machine learning models can achieve high predictive performance, their decision-making processes are often difficult for users to understand. This study investigates the application of explainable artificial intelligence (XAI) techniques for improving the transparency and reliability of machine learning systems. The research examines approaches for identifying important input features, explaining individual predictions, and communicating model decisions to end users. Different interpretability methods can be evaluated according to explanation consistency, computational efficiency, and comprehensibility. The study emphasizes the importance of explainability in applications where automated decisions may significantly influence users or organizational processes. The findings demonstrate that incorporating interpretability mechanisms can improve user confidence, facilitate model validation, and support responsible deployment of AI-based systems.