Explainable AI: Interpreting Deep Learning Models for Decision Support
Keywords:
Explainable AI, LIME, interpretabilityAbstract
Deep learning affects industry, hence XAI matters. Transparent deep learning models improve AI-driven decision support system interpretability. SHAP, LIME, and model-specific interpretability describe complicated AI system judgments. SHAP rates cooperative game theory predictions. It calculates each feature's minimal model decision-making effect. Locally interpretable surrogate forecasts resemble LIME black-box results. Model conduct may confirm expectations and reveal weaknesses.
Saliency mapping and activation maximization increase model-specific interpretability transparency. Optimise network neuron or layer activation inputs using model prediction. Saliency maps reveal meaningful model input output gradients. Not all deep learning models operate with these approaches. XAI impacts several businesses. XAI improves diagnostic model interpretation, physician trust, and regulation. Medical imaging model XAI predicts illness without pathology. Credit scoring by XAI ensures financial justice and compliance. Credit decision explanations decrease bias and increase auditability.
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