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When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial. Early warning systems can provide alerts when an unsafe situation is imminent (in the absence of corrective action). To reliably improve safety, these warning systems should have a provable false negative rate; i.e. of the situations that are unsafe, fewer than ϵ will occur without an alert. In this work, we present a framework that combines a statistical inferencearXiv:2109.14082v3 fatcat:vvpnzknpgvdflaohi2u3lgbngy