Predicting Critical Path of Labor Dispute Resolution in Legal Domain by Machine Learning Models Based on SHapley Additive exPlanations and Soft Voting Strategy

Jianhua Guan, Zuguo Yu (), Yongan Liao, Runbin Tang, Ming Duan and Guosheng Han
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Jianhua Guan: National Center for Applied Mathematics in Hunan, Xiangtan University, Xiangtan 411105, China
Zuguo Yu: National Center for Applied Mathematics in Hunan, Xiangtan University, Xiangtan 411105, China
Yongan Liao: Faculty of Law, Xiangtan University, Xiangtan 411105, China
Runbin Tang: School of Mathematics Science, Chongqing Normal University, Chongqing 401331, China
Ming Duan: Faculty of Law, Xiangtan University, Xiangtan 411105, China
Guosheng Han: National Center for Applied Mathematics in Hunan, Xiangtan University, Xiangtan 411105, China

Mathematics, 2024, vol. 12, issue 2, 1-17

Abstract: The labor dispute is one of the most common civil disputes. It can be resolved in the order of the following steps, which include mediation in arbitration, arbitration award, first-instance mediation, first-instance judgment, and second-instance judgment. The process can cease at any step when it is successfully resolved. In recent years, due to the increasing rights awareness of employees, the number of labor disputes has been rising annually. However, resolving labor disputes is time-consuming and labor-intensive, which brings a heavy burden to employees and dispute resolution institutions. Using artificial intelligence algorithms to identify and predict the critical path of labor dispute resolution is helpful for saving resources and improving the efficiency of, and reducing the cost of dispute resolution. In this study, a machine learning approach based on Shapley Additive exPlanations (SHAP) and a soft voting strategy is applied to predict the critical path of labor dispute resolution. We name our approach LDMLSV (stands for Labor Dispute Machine Learning based on SHapley additive exPlanations and Voting). This approach employs three machine learning models (Random Forest, Extra Trees, and CatBoost) and then integrates them using a soft voting strategy. Additionally, SHAP is used to explain the model and analyze the feature contribution. Based on the ranking of feature importance obtained from SHAP and an incremental feature selection method, we obtained an optimal feature subset comprising 33 features. The LDMLSV achieves an accuracy of 0.90 on this optimal feature subset. Therefore, the proposed approach is a highly effective method for predicting the critical path of labor dispute resolution.

Keywords: labor dispute resolution; critical path prediction; machine learning model; SHapley Additive exPlanations; soft voting strategy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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