  For machine learning components used as part of autonomous systems (AS) in
carrying out critical tasks it is crucial that assurance of the models can be
maintained in the face of post-deployment changes (such as changes in the
operating environment of the system). A critical part of this is to be able to
monitor when the performance of the model at runtime (as a result of changes)
poses a safety risk to the system. This is a particularly difficult challenge
when ground truth is unavailable at runtime. In this paper we introduce a
process for creating safety monitors for ML components through the use of
degraded datasets and machine learning. The safety monitor that is created is
deployed to the AS in parallel to the ML component to provide a prediction of
the safety risk associated with the model output. We demonstrate the viability
of our approach through some initial experiments using publicly available speed
sign datasets.
