Current legal outcome prediction models - a staple of legal NLP - do not
explain their reasoning. However, to employ these models in the real world,
human legal actors need to be able to understand their decisions. In the case
of common law, legal practitioners reason towards the outcome of a case by
referring to past case law, known as precedent. We contend that precedent is,
therefore, a natural way of facilitating explainability for legal NLP models.
In this paper, we contribute a novel method for identifying the precedent
employed by legal outcome prediction models. Furthermore, by developing a
taxonomy of legal precedent, we are able to compare human judges and our models
with respect to the different types of precedent they rely on. We find that
while the models learn to predict outcomes reasonably well, their use of
precedent is unlike that of human judges.

本文提出了一种新颖的方法来识别法律结果预测模型使用的先例，并开发了法律先例的分类法，从而能够比较人类法官和我们的模型在依赖的不同类型的先例方面的差异。我们发现，虽然这些模型在预测结果方面表现良好，但它们对先例的使用与人类法官不同。