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SHAP and LIME explanations vary with background data, sampling strategy, and runtime conditions, making them unreliable for regulated AI audits. A UK retail bank's TreeSHAP attribution changed order when rerun with a different background sample. SMOTE-based imbalance handling amplifies attribution instability, degrading audit-defense properties.
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