Predicting the number of days in court cases using artificial intelligence

• Published in: PloS one Vol. 17; no. 5; p. e0269008
• Main Authors: de Oliveira, Raphael Souza, Reis, Amilton Sales, Sperandio Nascimento, Erick Giovani
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26.05.2022; Public Library of Science (PLoS)
• Subjects: Actions and defenses; Algorithms; Analysis; Artificial intelligence; Computer and Information Sciences; Datasets; Decision making; Engineering and Technology; Evaluation; Learning algorithms; Literature reviews; Machine learning; Management; Mean square errors; Multilayer perceptrons; Neural networks; Physical Sciences; Portuguese language; Principles; Research and Analysis Methods; Stacking; Support vector machines; Time series; Brazil
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0269008

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 R 2 of 0.819 and when transformed into levels, an accuracy of 84%.