Formally Explaining Decision Tree Models with Answer Set Programming

2026-01-07

Electronic Proceedings in Theoretical Computer Science

AI Summary

This work addresses the limited interpretability of complex decision tree models—such as random forests and gradient-boosted trees—in safety-critical applications, where formal justifications for predictions are essential. To overcome this challenge, the authors propose a novel approach based on Answer Set Programming (ASP) that automatically generates diverse logical explanations, including sufficient, contrastive, majority-based, and tree-specific justifications. Compared to SAT-based methods, ASP offers greater flexibility in encoding user preferences and enables the enumeration of all feasible explanations, thereby significantly enhancing both the expressiveness and completeness of the generated justifications. Empirical evaluation across multiple datasets demonstrates the effectiveness of the proposed method in producing varied, formally verifiable explanations, while a systematic analysis highlights its strengths and limitations.

Technology Category

Constraint Satisfaction and Optimization: Satisfiability Modulo TheoriesReasoning under Uncertainty: Other Foundations of Reasoning under UncertaintyKnowledge Representation and Reasoning: Automated Reasoning and Theorem Proving

Application Category

User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalizationSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Decision tree models, including random forests and gradient-boosted decision trees, are widely used in machine learning due to their high predictive performance. However, their complex structures often make them difficult to interpret, especially in safety-critical applications where model decisions require formal justification. Recent work has demonstrated that logical and abductive explanations can be derived through automated reasoning techniques. In this paper, we propose a method for generating various types of explanations, namely, sufficient, contrastive, majority, and tree-specific explanations, using Answer Set Programming (ASP). Compared to SAT-based approaches, our ASP-based method offers greater flexibility in encoding user preferences and supports enumeration of all possible explanations. We empirically evaluate the approach on a diverse set of datasets and demonstrate its effectiveness and limitations compared to existing methods.

Problem

• decision tree
• model interpretability
• formal explanation
• safety-critical applications
• explainable AI

Innovation

• Answer Set Programming
• Explainable AI
• Decision Trees
• Formal Explanation
• Abductive Reasoning