Interpretable Legal Judgment Prediction Based On Improved Conditional Classification Tree

XAI (eXplainable Artificial Intelligence) has been an important cross domain topic between social sciences and artificial intelligence. Especially in the field of Legal Judgment Prediction (LJP), the computer systems aim to predict the judgments based on the facts of legal cases. The features of the subject matters, the subjects' behaviors, and the objective results are highly related to the crimes and punishments. Then the results should be coarsely explainable to people. However, many machine learning algorithms cannot make full use of such information and cannot give people the explaination for the results of LJP. In this paper, an Interpretable Conditional Classification Tree model (ICCT) is proposed to study the multi-class problem in LJP. Our model uses the prior information to recursively generate tree nodes. A feature search method for the feature domain construction, a data clustering algorithm and a grouping algorithm for tree node construction are proposed. The growth processes of the conditional classification tree realize the transition from coarse-grained classification to fine-grained classification which is called multi-granularity. The experimental results show the ICCT which has better interpretability achieves better performances over the baselines on the judgment prediction tasks.