Explainable artificial intelligence has rapidly emerged since lawmakers have
started requiring interpretable models for safety-critical domains.
Concept-based neural networks have arisen as explainable-by-design methods as
they leverage human-understandable symbols (i.e. concepts) to predict class
memberships. However, most of these approaches focus on the identification of
the most relevant concepts but do not provide concise, formal explanations of
how such concepts are leveraged by the classifier to make predictions. In this
paper, we propose a novel end-to-end differentiable approach enabling the
extraction of logic explanations from neural networks using the formalism of
First-Order Logic. The method relies on an entropy-based criterion which
automatically identifies the most relevant concepts. We consider four different
case studies to demonstrate that: (i) this entropy-based criterion enables the
distillation of concise logic explanations in safety-critical domains from
clinical data to computer vision; (ii) the proposed approach outperforms
state-of-the-art white-box models in terms of classification accuracy and
matches black box performances.

该论文提出了一种基于熵的标准及一种新的端到端可微分的方法，用于从概念神经网络中提取逻辑解释，该方法使用一阶逻辑的形式化定义，并考虑了四个案例研究来证明这种熵基准能够从临床数据到计算机视觉中，在安全关键领域中提取简洁的逻辑解释。