Predicting pre-trial detention
outcomes in the Brazilian Supreme Court (pp116-130)
Thiago Raulino Dal Pont, Isabela Cristina Sabo, Pablo Ernesto Vigneaux
Wilton, Victor Ara�jo de Menezes, Rafael Copetti, Luciano Zambrota, Pablo
Proc�pio Martins, Edjandir Corr�a Costa, Edimeia Liliani Schnitzler, Paloma
Maria Santos, Rodrigo Rafael Cunha, Gerson Bovi Kaster, Aires Jos� Rover
doi:
https://doi.org/10.26421/JDI3.1-2
Abstracts:
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.
Key words:
E-justice, Criminal Law, pre-trial detention, text
classification, association rules, machine learning
