FAIR: A Causal Framework for Accurately Inferring Judgments Reversals

TL;DR

This paper introduces FAIR, a causal framework that improves legal judgment prediction by mining and integrating causal factors of judgment reversals, enhancing model accuracy and interpretability.

Contribution

The paper presents a novel causal inference-based framework for identifying and utilizing causes of judgment reversals in legal AI, improving prediction performance and explainability.

Findings

FAIR effectively identifies critical factors in judgment reversals.

Incorporating causal relationships improves neural network accuracy.

Large language models still have generalization issues in legal tasks.

Abstract

Artificial intelligence researchers have made significant advances in legal intelligence in recent years. However, the existing studies have not focused on the important value embedded in judgments reversals, which limits the improvement of the efficiency of legal intelligence. In this paper, we propose a causal Framework for Accurately Inferring case Reversals (FAIR), which models the problem of judgments reversals based on real Chinese judgments. We mine the causes of judgments reversals by causal inference methods and inject the obtained causal relationships into the neural network as a priori knowledge. And then, our framework is validated on a challenging dataset as a legal judgment prediction task. The experimental results show that our framework can tap the most critical factors in judgments reversal, and the obtained causal relationships can effectively improve the neural network's performance. In addition, we discuss the generalization ability of large language models for legal intelligence tasks using ChatGPT as an example. Our experiment has found that the generalization ability of large language models still has defects, and mining causal relationships can effectively improve the accuracy and explain ability of model predictions.