Comparative Analysis of Techniques for Model Explainability and Interpretable Deep Learning

Abstract

As deep learning models become increasingly complex and integrated into critical applications, the need for transparency and interpretability has never been more pressing. This paper presents a comprehensive comparative analysis of various techniques aimed at enhancing model explainability and interpretability in deep learning. We systematically evaluate methods such as feature attribution, saliency maps, LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and model-agnostic approaches like attention mechanisms and rule-based systems. Our analysis highlights the strengths and limitations of each technique, considering factors such as computational efficiency, applicability to different model architectures, and the quality of explanations provided. Additionally, we discuss the trade-offs between interpretability and model performance, offering insights into how these techniques can be effectively utilized to balance transparency with predictive accuracy. Through empirical evaluation on a range of benchmark datasets and deep learning models, this study aims to guide researchers and practitioners in selecting appropriate techniques for their specific needs and fostering the development of more interpretable and trustworthy AI systems.