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

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 factor...

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Autores principales:	Decheng Hsieh, Lieuhen Chen, Taiping Sun
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	discretionary damages of mental suffering fatal car accident cases legal judgment prediction mental suffering damages relevant legal factors Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/a71f43625c8b4be7b02c04571bc3c930
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spelling	oai:doaj.org-article:a71f43625c8b4be7b02c04571bc3c9302021-11-11T15:23:46ZLegal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases10.3390/app1121103612076-3417https://doaj.org/article/a71f43625c8b4be7b02c04571bc3c9302021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10361https://doaj.org/toc/2076-3417The 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.Decheng HsiehLieuhen ChenTaiping SunMDPI AGarticlediscretionary damages of mental sufferingfatal car accident caseslegal judgment predictionmental suffering damagesrelevant legal factorsTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10361, p 10361 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	discretionary damages of mental suffering fatal car accident cases legal judgment prediction mental suffering damages relevant legal factors Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	discretionary damages of mental suffering fatal car accident cases legal judgment prediction mental suffering damages relevant legal factors Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Decheng Hsieh Lieuhen Chen Taiping Sun Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases
description	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.
format	article
author	Decheng Hsieh Lieuhen Chen Taiping Sun
author_facet	Decheng Hsieh Lieuhen Chen Taiping Sun
author_sort	Decheng Hsieh
title	Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases
title_short	Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases
title_full	Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases
title_fullStr	Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases
title_full_unstemmed	Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases
title_sort	legal judgment prediction based on machine learning: predicting the discretionary damages of mental suffering in fatal car accident cases
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a71f43625c8b4be7b02c04571bc3c930
work_keys_str_mv	AT dechenghsieh legaljudgmentpredictionbasedonmachinelearningpredictingthediscretionarydamagesofmentalsufferinginfatalcaraccidentcases AT lieuhenchen legaljudgmentpredictionbasedonmachinelearningpredictingthediscretionarydamagesofmentalsufferinginfatalcaraccidentcases AT taipingsun legaljudgmentpredictionbasedonmachinelearningpredictingthediscretionarydamagesofmentalsufferinginfatalcaraccidentcases
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Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases

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