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Predicting Brazilian Court Decisions.

André Lage-Freitas1,2, Héctor Allende-Cid2,3, Orivaldo Santana2,4

  • 1Universidade Federal de Alagoas, Maceió, Brazil.

Peerj. Computer Science
|May 2, 2022
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Summary

Predicting Brazilian court decisions is challenging due to legal complexity. This study introduces a machine learning approach, achieving an 80.2% F1-score in predicting case outcomes and unanimity.

Keywords:
Artificial intelligenceJurimetricsLawLegalLegal informaticsLegal outcome forecastLitigation predictionMachine learningPredictive algorithms

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Area of Science:

  • Computational Law
  • Artificial Intelligence in Legal Studies
  • Machine Learning for Judicial Prediction

Background:

  • Predicting legal case outcomes is crucial for legal professionals but is hindered by the complexity of legal systems and vast amounts of data.
  • Extracting relevant information from numerous cases and documents presents a significant challenge in legal decision prediction.

Purpose of the Study:

  • To introduce a novel approach for predicting Brazilian court decision outcomes, including the likelihood of unanimous decisions.
  • To address the challenges of legal system complexity and data volume in judicial prediction.

Main Methods:

  • Utilized a range of machine learning algorithms, including classifiers and advanced Deep Learning models.
  • Developed and evaluated a working prototype using 4,043 cases from a Brazilian court.

Main Results:

  • Achieved an F1-score performance of approximately 80.2% in predicting court decision outcomes.
  • Demonstrated the feasibility of applying machine learning to predict Brazilian judicial decisions.

Conclusions:

  • This study presents the first known methods for predicting Brazilian court decision outcomes using machine learning.
  • The developed approach shows significant promise for enhancing legal professionals' ability to anticipate case results.