User Driven Model Adjustment via Boolean Rule Explanations

TL;DR

This paper introduces an interactive method that allows users to modify machine learning decision boundaries using Boolean rule explanations, enabling instant updates without retraining and improving system adaptability.

Contribution

It presents a novel overlay approach that integrates user feedback with ML predictions through Boolean rules, facilitating real-time model adjustments without retraining.

Findings

Enables instant decision boundary modifications via user feedback.

Reduces the need for extensive retraining with less data.

Supports dynamic updates in AI systems for current policies.

Abstract

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.