Bridging AI and Human Understanding: Interpretable Deep Learning in Practice

Abstract

Deep learning influences industry; hence, explainable artificial intelligence (XAI) is significant. Transparent deep learning models enhance the interpretability of AI-driven decision support systems. SHAP, LIME, and model-specific interpretability elucidate intricate AI system decisions. SHAP evaluates predictions in cooperative game theory. It assesses the least decision-making impact of each feature in the model. Locally interpretable surrogate forecasts are analogous to LIME black-box outcomes. Model behavior may validate expectations and expose deficiencies.