The field of Explainable AI (XAI) is seeking to shed light on the inner workings of complex AI models and uncover the rationale behind their decisions. One of the models gaining attention are probabilistic circuits (PCs), which are a general and unified framework for tractable probabilistic models that support efficient computation of various probabilistic queries. Probabilistic circuits guarantee inference that is polynomial in the size of the circuit. In this paper, we improve the explainability of probabilistic circuits by computing a comprehensible, readable logical theory that covers the high-density regions generated by a PC. To achieve this, pruning approaches based on generative significance are used in a new method called PUTPUT (Probabilistic circuit Understanding Through Pruning Underlying logical Theories). The method is applied to a real world use case where music playlists are automatically generated and expressed as readable (database) queries. Evaluation shows that this approach can effectively produce a comprehensible logical theory that describes the high-density regions of a PC and outperforms state of the art methods when exploring the performance-comprehensibility trade-off.

翻译：可解释人工智能（XAI）领域致力于揭示复杂AI模型的内部工作原理，并阐明其决策背后的逻辑依据。概率电路作为一种通用且统一的框架，能够高效支持各类概率查询的可靠计算，正受到学界广泛关注。这类模型可确保推理复杂度与电路规模呈多项式关系。本文通过计算覆盖概率电路生成的高密度区域的简洁可读逻辑理论，提升了概率电路的可解释性。我们提出了一种名为PUTPUT（通过剪枝底层逻辑理论理解概率电路）的新方法，其核心是基于生成显著性的剪枝策略。该方法被应用于真实场景：将自动生成的音乐播放列表表达为可读的数据库查询语句。实验评估表明，本方法能有效生成描述概率电路高密度区域的可理解逻辑理论，并在性能-可理解性权衡评估中优于现有最优方法。