Explainable legal judgment prediction via concept tree and concept forest reasoning with collegiate bench mechanism

Abstract

Nowadays artificial intelligence (AI) has been applied in many high-stake decision-making tasks. The black box AI models which are lack of explainability can cause serious problems in practice. In the justice, an explainable model becomes more and more important. Since tree-based machine learning models are explainable, we propose an explainable legal judgment prediction model using concept trees with collegiate bench mechanism in this paper. A concept tree is constructed to check the classification labels predicted by the original multi-classifier. A revising process is designed to deal with the scenario when the results of the original multi-classifier and the concept trees are conflicted. Meanwhile, the concept trees grow into concept forest because of the existence of arbitration classifiers. The judicial judgment process is simulated, which not only makes the good classification performance with collegiate bench mechanism, but also has the model explanation from the features in the conceptual levels. The experiments validate the validity of our model with both better explainability and better accuracy.