The interpretability of model has become one of the obstacles to its wide
application in the high-stake fields. The usual way to obtain interpretability
is to build a black-box first and then explain it using the post-hoc methods.
However, the explanations provided by the post-hoc method are not always
reliable. Instead, we design an intrinsically interpretable model based on
RRL(Rule Representation Learner) for the Lending Club dataset. Specifically,
features can be divided into three categories according to their
characteristics of themselves and build three sub-networks respectively, each
of which is similar to a neural network with a single hidden layer but can be
equivalently converted into a set of rules. During the training, we learned
tricks from previous research to effectively train binary weights. Finally, our
model is compared with the tree-based model. The results show that our model is
much better than the interpretable decision tree in performance and close to
other black-box, which is of practical significance to both financial
institutions and borrowers. More importantly, our model is used to test the
correctness of the explanations generated by the post-hoc method, the results
show that the post-hoc method is not always reliable.

本文提出了一种基于 RRL 的内在可解释模型，其可对每个特征分成三类构建三个类似于神经网络的子网络，每个子网络均可等效转换为一组规则，并通过学习二进制权重的技巧有效地训练，相比可解释的决策树在性能上更优，接近于其他黑匣子，是对金融机构和借款人具有实际意义的。同时，本模型还用于测试后评方法生成的解释的准确性，结果表明后评方法不总是可靠的。