  AI solutions are heavily dependant on the quality and accuracy of the input
training data, however the training data may not always fully reflect the most
up-to-date policy landscape or may be missing business logic. The advances in
explainability have opened the possibility of allowing users to interact with
interpretable explanations of ML predictions in order to inject modifications
or constraints that more accurately reflect current realities of the system. In
this paper, we present a solution which leverages the predictive power of ML
models while allowing the user to specify modifications to decision boundaries.
Our interactive overlay approach achieves this goal without requiring model
retraining, making it appropriate for systems that need to apply instant
changes to their decision making. We demonstrate that user feedback rules can
be layered with the ML predictions to provide immediate changes which in turn
supports learning with less data.
