Predicting the number of days in court cases using artificial intelligence

Abstract

Brazilian legal system prescribes means of ensuring the prompt processing of court cases, such as the principle of reasonable process duration, the principle of celerity, procedural economy, and due legal process, with a view to optimizing procedural progress. In this context, one of the great challenges of the Brazilian judiciary is to predict the duration of legal cases based on information such as the judge, lawyers, parties involved, subject, monetary values of the case, starting date of the case, etc. Recently, there has been great interest in estimating the duration of various types of events using artificial intelligence algorithms to predict future behaviors based on time series. Thus, this study presents a proof-of-concept for creating and demonstrating a mechanism for predicting the amount of time, after the case is argued in court (time when a case is made available for the magistrate to make the decision), for the magistrate to issue a ruling. Cases from a Regional Labor Court were used as the database, with preparation data in two ways (original and discretization), to test seven machine learning techniques (i) Multilayer Perceptron (MLP); (ii) Gradient Boosting; (iii) Adaboost; (iv) Regressive Stacking; (v) Stacking Regressor with MLP; (vi) Regressive Stacking with Gradient Boosting; and (vii) Support Vector Regression (SVR), and determine which gives the best results. After executing the runs, it was identified that the adaboost technique excelled in the task of estimating the duration for issuing a ruling, as it had the best performance among the tested techniques. Thus, this study shows that it is possible to use machine learning techniques to perform this type of prediction, for the test data set, with an R2 of 0.819 and when transformed into levels, an accuracy of 84%.

Investigation of Lawsuit Process Duration: A Machine Learning and Process Mining Approach

“…The first one concerns the prediction of outcomes from judgments [17][18][19][20] and classification of legal documents [21]. The second branch of works investigates the duration of a case in machine learning [16,22,23], and a mathematical [24][25][26][27] perspective. The last category encompasses works that analyze the lawsuits from a process mining perspective [28][29][30].…”

“…Similarly, Oliveira, Reis Jr and Nascimento [16] applied multiple machine learning models to predict the required time for a judge to present a decision once the case is argued in court. They employed variables, such as the identification of the magistrate and lawyers involved and the case's monetary value.…”

“…However, we drive the analysis from a time perspective, incorporating the found clusters into the pool of features that will serve as input for the regression models. On one hand, we measure the capacity of the models for approximating the case overall time, such as in [22] and [16]. On the other hand, we endeavor to measure the impact of these different aspects in the task, such as in [26] and [27] through feature correlation and importance.…”

“…The linear regression is the simplest and serves as a base model. The other two models have successfully been used in multiple regression tasks [15,16]. We used a dataset with 61,553 lawsuits from first-instance Brazilian Labor courts.…”

A smart model to minimize the waiting time for the cases in Egyptian Economic courts

“…By analyzing large volumes of data, machine learning algorithms can identify patterns and make predictions, helping judges and court administrators make more informed decisions. Using machine learning in the Egyptian Economic Courts can enhance the efficiency and accuracy of case management and decision-making processes, ultimately leading to fairer resolutions for economic disputes [26,27].…”