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While machine learning systems show high success rate in many complex tasks,research shows they can also fail in very unexpected situations. Rise ofmachine learning products in safety-critical industries cause an increase inattention in evaluating model robustness and estimating failure probability inmachine learning systems. In this work, we propose a design to train a studentmodel -- a failure predictor -- to predict the main model's error for inputinstances based on their saliency map. We implement and review the preliminaryresults of our failure predictor model on an autonomous vehicle steeringcontrol system as an example of safety-critical applications.

Predicting Model Failure using Saliency Maps in Autonomous Driving Systems