Explainable AI for Large-Scale Predictive Systems: Techniques, Applications, and Future Directions

Abstract

This article provides a comprehensive examination of Explainable Artificial Intelligence (XAI) techniques and their applications in large-scale predictive systems. The article explores both model-agnostic and model-specific approaches, examining their effectiveness in various domains including healthcare, finance, and transportation. The article explores fundamental XAI concepts, historical development, and current taxonomies while addressing crucial regulatory and ethical considerations. The article examines feature importance methods, partial dependence plots, SHAP values, LIME, and counterfactual explanations as key model-agnostic techniques. It further delves into model-specific approaches including decision tree interpretability, neural network visualization, attention mechanisms, rule extraction methods, and architecture-specific approaches. The article extensively covers domain applications, highlighting how XAI enhances transparency and trust in critical sectors. The article also addresses significant challenges including scalability issues, interpretation complexity, computational overhead, accuracy-explainability trade-offs, and human factors in XAI implementation. This article contributes to the understanding of XAI's current state and future directions in large-scale predictive systems.