Enhancing the Performance and Interpretability of Machine LearningModels Through Explainable Artificial Intelligence Techniques

Abstract

The rapid advancement of Machine Learning (ML) models has led to remarkable improvements in predictive accuracy and automation across various domains. However, the increasing complexity of these models has introduced challenges in understanding and interpreting their decision-making processes. Explainable Artificial Intelligence (XAI) has emerged as a critical field aimed at improving the interpretability and transparency of ML models without compromising their performance. This paper explores how XAI techniques can be integrated into ML models to enhance both their predictive accuracy and interpretability. Through a systematic literature review, we analyze the most effective XAI methods and their impact on model performance. Experimental results demonstrate that incorporating XAI techniques, such as SHAP, LIME, and saliency maps, improves model trustworthiness and user confidence while maintaining high accuracy. This study contributes to a deeper understanding of the trade-offs between model complexity, accuracy, and interpretability, offering practical recommendations for implementing XAI in real-world applications.