An Interpretable Rule Creation Method for Black-Box Models based on Surrogate Trees -- SRules

TL;DR

This paper introduces SRules, a surrogate decision tree-based method that enhances the interpretability of black-box models by generating concise, meaningful, and confidence-quantified rules, improving transparency in AI decision-making.

Contribution

The paper presents a novel framework for creating interpretable rules from surrogate decision trees that approximate complex models, balancing accuracy, coverage, and interpretability.

Findings

SRules outperforms existing techniques in interpretability and accuracy

Allows adjustable parameters for tailored interpretability and coverage

Enables creation of specific rules for sub-models

Abstract

As artificial intelligence (AI) systems become increasingly integrated into critical decision-making processes, the need for transparent and interpretable models has become paramount. In this article we present a new ruleset creation method based on surrogate decision trees (SRules), designed to improve the interpretability of black-box machine learning models. SRules balances the accuracy, coverage, and interpretability of machine learning models by recursively creating surrogate interpretable decision tree models that approximate the decision boundaries of a complex model. We propose a systematic framework for generating concise and meaningful rules from these surrogate models, allowing stakeholders to understand and trust the AI system's decision-making process. Our approach not only provides interpretable rules, but also quantifies the confidence and coverage of these rules. The proposed model allows to adjust its parameters to counteract the lack of interpretability by precision and coverage by allowing a near perfect fit and high interpretability of some parts of the model . The results show that SRules improves on other state-of-the-art techniques and introduces the possibility of creating highly interpretable specific rules for specific sub-parts of the model.