Identifying Jurisprudential Divergences with Artificial Intelligence Techniques to Support Judicial Information Systems - Castro, Marcella Queiroz de; Ralha, Célia Ghedini (Companion Proceedings of the Brazilian Symposium on Information Systems (SBSI); 2025: Companion Proceedings of the 21st Brazilian Symposium on Information Systems, 2025)
This work explores the application of Natural Language Processing and Machine Learning techniques to detect jurisprudential divergences, contributing to greater legal certainty. The methodology encompasses data preprocessing of judicial summaries, the use of the Word2Vec neural network technique for word embedding, and the clustering of 3,163 court rulings using K-Means to identify semantic similarities and divergences. By presenting examples of discrepant decisions, the study demonstrates the potential of the approach to promote jurisprudential uniformity. The findings support the development of information systems that assist in legal interpretation and enhance the predictability of decisions, ultimately fostering a fairer society with greater equity, inclusion, and diversity.