Enhancing Rule-Based Explanations via Cognitive Bias Towards Semantic Relevance

Abstract

As interest in explainable artificial intelligence (XAI) continues to grow, a critical gap remains between the explanations generated by models and their interpretability by human users, particularly when cognitive biases and semantic relevance are not adequately addressed. This paper introduces CoRIfEE-Rel, a novel human-centered meta-XAI method aimed at bridging this gap by producing rule-based explanations that are both interpretable and semantically aligned with the target domain concept. CoRIfEE-Rel synthesizes outputs from a diverse pool of interpretable models and employs a knowledge graph-driven heuristic that combines semantic relevance with traditional rule learning metrics. This approach ensures that the resulting explanations are deeply tied to core domain concepts while maintaining the clarity and structure needed for human understanding. Empirical evaluations conducted across multiple datasets demonstrate that CoRIfEE-Rel achieves higher semantic relevance than random forest and JRip rule-based explanations without notable compromises in predictive accuracy. The results highlight the ability of CoRIfEE-Rel to generate rule-based explanations that are semantically related to the target concepts while maintaining predictive performance.