A new evidence sufficiency model with four dimensions and seven proxy categories enables monitoring of ML risk systems under delayed ground truth, detecting covariate and mixed drift but not concept drift without feature changes.
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The paper proposes the IARC-TS protocol that combines drift monitoring, uncertainty quantification, and stress tests to generate reproducible robustness evidence for industrial time series models mapped to EU AI Act obligations.
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Evidence Sufficiency Under Delayed Ground Truth: Proxy Monitoring for Risk Decision Systems
A new evidence sufficiency model with four dimensions and seven proxy categories enables monitoring of ML risk systems under delayed ground truth, detecting covariate and mixed drift but not concept drift without feature changes.
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Industrial AI Robustness Card for Time Series Models
The paper proposes the IARC-TS protocol that combines drift monitoring, uncertainty quantification, and stress tests to generate reproducible robustness evidence for industrial time series models mapped to EU AI Act obligations.