Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importance

TL;DR

This paper introduces a method to generate rule-based explanations that closely mimic the decision-making process of black box models by combining rule induction with feature importance from XAI methods, validated on multiple datasets.

Contribution

It proposes a novel explanation fusion approach that ensures rule-based surrogate models replicate black box model decisions and feature importance, enhancing interpretability and consistency.

Findings

Method achieves high decision consistency with black box models.

Extensive evaluation on 30 benchmark datasets supports effectiveness.

Provides both global and local rule-based explanations.

Abstract

Rule-based models offer a human-understandable representation, i.e. they are interpretable. For this reason, they are used to explain the decisions of non-interpretable complex models, referred to as black box models. The generation of such explanations involves the approximation of a black box model by a rule-based model. To date, however, it has not been investigated whether the rule-based model makes decisions in the same way as the black box model it approximates. Decision making in the same way is understood in this work as the consistency of decisions and the consistency of the most important attributes used for decision making. This study proposes a novel approach ensuring that the rule-based surrogate model mimics the performance of the black box model. The proposed solution performs an explanation fusion involving rule generation and taking into account the feature importance determined by the selected XAI methods for the black box model being explained. The result of the method can be both global and local rule-based explanations. The quality of the proposed solution was verified by extensive analysis on 30 tabular benchmark datasets representing classification problems. Evaluation included comparison with the reference method and an illustrative case study. In addition, the paper discusses the possible pathways for the application of the rule-based approach in XAI and how rule-based explanations, including the proposed method, meet the user perspective and requirements for both content and presentation. The software created and a detailed report containing the full experimental results are available on the GitHub repository (https://github.com/ruleminer/FI-rules4XAI ).