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FastSHAP: Real-Time Shapley Value Estimation
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Shapley values are widely used to explain black-box models, but they are costly to calculate because they require many model evaluations. We introduce FastSHAP, a method for estimating Shapley values in a single forward pass using a learned explainer model. FastSHAP amortizes the cost of explaining many inputs via a learning approach inspired by the Shapley value's weighted least squares characterization, and it can be trained using standard stochastic gradient optimization. We compare FastSHAP to existing estimation approaches, revealing that it generates high-quality explanations with orders of magnitude speedup.
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Cited by 1 Pith paper
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ShaTS: A Shapley-based Explainability Method for Time Series Artificial Intelligence Models applied to Anomaly Detection in Industrial Internet of Things
ShaTS computes Shapley attributions directly on semantic groups of time-series features, improving sensor- and process-level anomaly explanations over post hoc SHAP on the SWaT dataset.
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