Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases

Abstract

The discretionary damage of mental suffering in fatal car accident cases in Taiwan is subjective, uncertain, and unpredictable; thus, plaintiffs, defendants, and their lawyers find it difficult to judge whether spending much of their money and time on the lawsuit is worthwhile and which legal factors judges will consider important and dominant when they are assessing the mental suffering damages. To address these problems, we propose k-nearest neighbor, classification and regression trees, and random forests as learning algorithms for regression to build optimal predictive models. In addition, we reveal the importance ranking of legal factors by permutation feature importance. The experimental results show that the random forest model outperformed the other models and achieved good performance, and “the mental suffering damages that plaintiff claims” and “the age of the victim” play important roles in assessments of mental suffering damages in fatal car accident cases in Taiwan. Therefore, litigants and their lawyers can predict the discretionary damages of mental suffering in advance and wisely decide whether they should litigate or not, and then they can focus on the crucial legal factors and develop the best litigation strategy.

Predicting the Amount of Compensation for Harm Awarded by Courts Using Machine-Learning Algorithms

“…Nonetheless, the author did not distinguish between material and non-material damage. Hsieh et al (2021) focused on prediction of the amount awarded as compensation for non-material damage by the Taiwan Taichung District Court. As for the results, random forests outperformed KNN and CART.…”

“…Despite the undoubted practical and social relevance of the issue discussed, there are very few studies in the literature on the use of machinelearning tools for the prediction of the amount of compensation specifically for harm suffered (Dal Pont et al, 2023;Hsieh, Chen & Sun, 2021;Torres, Guterres & Celestino, 2023;Yeung, 2019). Therefore, the main contribution of this research is that this paper is the first to explain and predict monetary amounts awarded as compensation for harm suffered by applying machine-learning algorithms to a data set that is not limited to judgements pronounced in specific types of cases but involves a distinctively heterogeneous set of cases.…”