Explainability-Driven Quality Assessment for Rule-Based Systems
This addresses the need for more efficient and interpretable rule refinement in domains like finance, though it is incremental as it builds on existing explanation methods.
The paper tackles the problem of improving rule quality in knowledge-based systems by introducing an explanation framework that uses data-driven insights to refine rules, demonstrated in a finance use case.
This paper introduces an explanation framework designed to enhance the quality of rules in knowledge-based reasoning systems based on dataset-driven insights. The traditional method for rule induction from data typically requires labor-intensive labeling and data-driven learning. This framework provides an alternative and instead allows for the data-driven refinement of existing rules: it generates explanations of rule inferences and leverages human interpretation to refine rules. It leverages four complementary explanation types: trace-based, contextual, contrastive, and counterfactual, providing diverse perspectives for debugging, validating, and ultimately refining rules. By embedding explainability into the reasoning architecture, the framework enables knowledge engineers to address inconsistencies, optimize thresholds, and ensure fairness, transparency, and interpretability in decision-making processes. Its practicality is demonstrated through a use case in finance.