Why Attribution Stability Matters More Than Attribution Accuracy

This story was originally published on HackerNoon at: https://hackernoon.com/why-attribution-stability-matters-more-than-attribution-accuracy. SHAP attribution accuracy is the wrong metric for regulated AI. σ_SHAP — variance across K rotated background samples — is the defensible alternative. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #explainable-ai, #shap-and-lime, #llmops, #ai-governance, #machine-learning, #regulated-ai-systems, #mlops, #data-science, and more. This story was written by: @karansehgal1997. Learn more about this writer by checking @karansehgal1997's about page, and for more stories, please visit hackernoon.com. 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.