Predicting pre-trial detention outcomes in the Brazilian Supreme Court

Brazil has a large prison population, which places it as the third country in the world with the most incarceration rate. In addition, the criminal caseload is increasing in Brazilian Judiciary, which is encouraging AI usage to advance in e-Justice. Within this context, the paper presents a case study with a dataset composed of 2,200 judgments from the Supreme Federal Court (STF) about pre-trial detention. These are cases in which a provisional prisoner requests for freedom through habeas corpus. We applied Machine Learning (ML) and Natural Language Processing (NLP) techniques to predict whether STF will release or not the provisional prisoner (text classification), and also to find a reliable association between the judgment outcome and the prisoners' crime and/or the judge responsible for the case (association rules). We obtained satisfactory results in both tasks. Classification results show that, among the models used, Convolutional Neural Network (CNN) is the best, with 95% accuracy and 0.91 F1-Score. Association results indicate that, among the rules generated, there is a high probability of drug law crimes leading to a dismissed habeas corpus (which means the maintenance of pre-trial detention). We concluded that STF has not interfered in first degree decisions about pre-trial detention and that it is necessary to discuss drug criminalization in Brazil. The main contribution of the paper is to provide models that can support judges and pre-trial detainees.