Explainable artificial intelligence (XAI) is one of the most intensively developed area of AI in recent years. It is also one of the most fragmented with multiple methods that focus on different aspects of explanations. This makes difficult to obtain the full spectrum of explanation at once in a compact and consistent way. To address this issue, we present Local Universal Explainer (LUX), which is a rule-based explainer that can generate factual, counterfactual and visual explanations. It is based on a modified version of decision tree algorithms that allows for oblique splits and integration with feature importance XAI methods such as SHAP or LIME. It does not use data generation in opposite to other algorithms, but is focused on selecting local concepts in a form of high-density clusters of real data that have the highest impact on forming the decision boundary of the explained model. We tested our method on real and synthetic datasets and compared it with state-of-the-art rule-based explainers such as LORE, EXPLAN and Anchor. Our method outperforms the existing approaches in terms of simplicity, global fidelity, representativeness, and consistency.

翻译：可解释人工智能（XAI）是近年来人工智能领域发展最为活跃的方向之一，同时也因众多方法聚焦于不同解释维度而呈现高度碎片化特征，这使得难以通过一种紧凑且一致的方式同时获取完整的解释谱系。为解决这一问题，我们提出局部通用解释器（LUX），这是一种基于规则的解释器，能够生成事实、反事实及可视化解释。该方法基于改进版决策树算法，支持斜向分割，并可与SHAP或LIME等特征重要性XAI方法集成。与其他算法不同，LUX不依赖数据生成，而是聚焦于从真实数据中选取高密度聚类形式的局部概念，这些概念对形成被解释模型的决策边界具有最大影响。我们在真实与合成数据集上验证了该方法，并与LORE、EXPLAN和Anchor等前沿基于规则的解释器进行了比较。实验结果表明，本方法在简洁性、全局保真度、代表性和一致性方面均优于现有方法。