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Machine Learning~(ML) has provided promising results in recent years acrossdifferent applications and domains. However, in many cases, qualities such asreliability or even safety need to be ensured. To this end, one importantaspect is to determine whether or not ML components are deployed in situationsthat are appropriate for their application scope. For components whoseenvironments are open and variable, for instance those found in autonomousvehicles, it is therefore important to monitor their operational situation todetermine its distance from the ML components' trained scope. If that distanceis deemed too great, the application may choose to consider the ML componentoutcome unreliable and switch to alternatives, e.g. using human operator inputinstead. SafeML is a model-agnostic approach for performing such monitoring,using distance measures based on statistical testing of the training andoperational datasets. Limitations in setting SafeML up properly include thelack of a systematic approach for determining, for a given application, howmany operational samples are needed to yield reliable distance information aswell as to determine an appropriate distance threshold. In this work, weaddress these limitations by providing a practical approach and demonstrate itsuse in a well known traffic sign recognition problem, and on an example usingthe CARLA open-source automotive simulator.

Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring