Using attention methods to predict judicial outcomes
This work addresses legal judgment prediction for the Brazilian legal system, but it is incremental as it applies existing methods to new data without major methodological breakthroughs.
The research tackled predicting judicial outcomes in the Brazilian legal system using AI classifiers on a dataset of second-degree murder and active corruption cases, finding that Regression Trees, Gated Recurring Units, and Hierarchical Attention Networks achieved higher metrics for different subsets and identified key words influencing verdicts.
Legal Judgment Prediction is one of the most acclaimed fields for the combined area of NLP, AI, and Law. By legal prediction we mean an intelligent systems capable to predict specific judicial characteristics, such as judicial outcome, a judicial class, predict an specific case. In this research, we have used AI classifiers to predict judicial outcomes in the Brazilian legal system. For this purpose, we developed a text crawler to extract data from the official Brazilian electronic legal systems. These texts formed a dataset of second-degree murder and active corruption cases. We applied different classifiers, such as Support Vector Machines and Neural Networks, to predict judicial outcomes by analyzing textual features from the dataset. Our research showed that Regression Trees, Gated Recurring Units and Hierarchical Attention Networks presented higher metrics for different subsets. As a final goal, we explored the weights of one of the algorithms, the Hierarchical Attention Networks, to find a sample of the most important words used to absolve or convict defendants.