Predicting Outcomes of Legal Cases based on Legal Factors using Classifiers

Predicting outcomes of legal cases may aid in the understanding of the judicial decision-making process. Outcomes can be predicted based on i) case-specific legal factors such as type of evidence ii) extra-legal factors such as the ideological direction of the court. The details of case-specific legal factors can be extracted from legal judgments. However, extracting these factors from legal texts is a tedious and time-consuming process. In this work, important factors affecting outcomes of murder related cases (taken from Delhi District Court) are identified and a database of 86 cases is prepared in order to use these factors as descriptors for outcome prediction. The outcome prediction is seen as a binary classification problem for classes ‘Acquittal’ and ‘Conviction’ of the accused person. Conventional machine learning classification algorithms are applied and Leave-one-out cross validation is used to produce the results. The performance of classifiers is evaluated and compared using metrics such as Accuracy, Precision, Recall, and F1 Score. The statistical distribution of features and the experimental results (Accuracy ranging from 85% to 92% and F1 Score from 86% to 92% for classifiers) show the success in identifying important factors of concerned cases and in turn predicting their outcomes.