Automatic Discovery of Controversial Legal Judgments by an Entropy-Based Measurement (S)

—The judgment of controversial cases has always been an important judicial issue, but it is not easy to discover them in practice. In this paper, based on 1,361,354 legal instruments data collected from China Judgments Online, we adopt a deep learning framework to classify 147 different kinds of crimes. The proposed method has three critical steps: 1) We adopt a deep learning model to predict crime categorization; 2) With the trained model, each case is given a score vector which represents the probability that it belongs to each crime; 3) With the probability score, we develop an entropy-based index to measure the controversy of each case. We find that the larger the entropy, the more inconsistent the result given by the model based on the first instance judgment. To verify the proposed entropy measure, we provide 1) two-sided evidence based on second instance judgments; 2) comparison with some baseline models. Both confirm the practical usefulness of the entropy measure. Our results indicate that the proposed framework has an ability to discover potentially controversial cases. It should be noted that the goal of this study is not to substitute the model result for the judge’s decision, but to provide a guiding reference for the judicial practice of sentencing.