Explainable AI Models for Transparent Decision-Making in Critical Systems

Abstract

As AI systems are increasingly used in critical domains such as healthcare and finance, the need for transparency and interpretability has become essential. This paper explores Explainable AI (XAI) techniques to improve trust and accountability in machine learning models. Methods such as SHAP, LIME, and attention mechanisms are evaluated for their effectiveness in explaining predictions. The study demonstrates how XAI enhances user confidence while maintaining model performance, making AI systems more reliable and ethically compliant.