User Driven Model Adjustment via Boolean Rule Explanations
This addresses the need for real-time model adjustments in systems where training data is outdated or incomplete, though it is incremental as it builds on existing explainability methods.
The paper tackles the problem of AI models not reflecting current policies or business logic by allowing users to modify decision boundaries via Boolean rule explanations, achieving instant changes without retraining and supporting learning with less data.
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.