Predicting Indian Supreme Court Judgments, Decisions, Or Appeals

TL;DR

This paper introduces eLegPredict, a machine learning model that predicts Indian Supreme Court decisions with 76% accuracy, aiding legal professionals in decision-making and case analysis.

Contribution

The paper presents the first legal prediction model specifically designed for Indian Supreme Court decisions, including an operational prototype and a mechanism for real-time predictions.

Findings

Achieved 76% accuracy (F1-score) on 3072 cases.

Developed an operational prototype for real-time case outcome prediction.

First model to predict Indian Supreme Court decisions.

Abstract

Legal predictive models are of enormous interest and value to legal community. The stakeholders, specially, the judges and attorneys can take the best advantages of these models to predict the case outcomes to further augment their future course of actions, for example speeding up the decision making, support the arguments, strengthening the defense, etc. However, accurately predicting the legal decisions and case outcomes is an arduous process, which involves several complex steps -- finding suitable bulk case documents, data extracting, cleansing and engineering, etc. Additionally, the legal complexity further adds to its intricacies. In this paper, we introduce our newly developed ML-enabled legal prediction model and its operational prototype, eLegPredict; which successfully predicts the Indian supreme court decisions. The eLegPredict is trained and tested over 3072 supreme court cases and has achieved 76% accuracy (F1-score). The eLegPredict is equipped with a mechanism to aid end users, where as soon as a document with new case description is dropped into a designated directory, the system quickly reads through its content and generates prediction. To our best understanding, eLegPredict is the first legal prediction model to predict Indian supreme court decisions.