SHAP attribution accuracy is the wrong metric for regulated AI. σ_SHAP — variance across K rotated background samples — is the defensible alternative.

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An adversarial explainer can choose a background dataset that makes the same model justify two opposite decisions. Attribution accuracy is not the goal — attribution stability is. σ_SHAP, measured across K rotated background samples, gives you a variance bound you can defend under regulatory challenge. Single-shot SHAP cannot.