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A 16-year-old developer created sage-explainer, a Python package that approximates prediction sensitivity to features for black-box models like random forests and XGBoost, offering more stable results than centered finite differences.
This paper introduces an architecture-aware explanation audit protocol for industrial visual inspection, demonstrating that the faithfulness of explanation methods is bounded by their structural compatibility with a model's native decision mechanism, using experiments on wafer map and anomaly detection datasets.