SHAP, a popular tool for explaining AI models, is not suitable for production environments due to its slow performance and inconsistent results. This matters because reliable explainability is crucial for real-time systems requiring deterministic outputs and audit trails, highlighting the need for integrated model architectures that generate explanations during the prediction process rather than as an add-on.
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