Explainable AI: Interpreting Deep Learning Models for Decision Support
Keywords:
Explainable AI, XAI, SHAP valuesAbstract
Why Explainable AI (XAI) matters: deep learning models affect industry. AI-driven decision support system interpretability is emphasized via deep learning model transparency. Model-specific interpretability, LIME, and SHAP characterize complicated AI system choices.
SHAP values rank prediction-based cooperative game theory qualities. This method calculates each feature's minimal model decision-making influence. Interpretable surrogate models close forecasts approach LIME black-box models locally. Based on model behavior, users may validate expectations and identify constraints.
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