Predicting delays in Indian lower courts using AutoML and Decision Forests

TL;DR

This study develops an AutoML-based decision forest model to predict delays in Indian lower courts using extensive case data, achieving over 81% accuracy, and demonstrates AI's potential in judicial reform.

Contribution

The paper introduces a novel application of AutoML and decision forests for predicting court delays in India, utilizing a large-scale dataset and providing open access to data and code.

Findings

Achieved 81.4% accuracy in delay prediction

Demonstrated feasibility of AI models for Indian courts

Provided open dataset and code for further research

Abstract

This paper presents a classification model that predicts delays in Indian lower courts based on case information available at filing. The model is built on a dataset of 4.2 million court cases filed in 2010 and their outcomes over a 10-year period. The data set is drawn from 7000+ lower courts in India. The authors employed AutoML to develop a multi-class classification model over all periods of pendency and then used binary decision forest classifiers to improve predictive accuracy for the classification of delays. The best model achieved an accuracy of 81.4%, and the precision, recall, and F1 were found to be 0.81. The study demonstrates the feasibility of AI models for predicting delays in Indian courts, based on relevant data points such as jurisdiction, court, judge, subject, and the parties involved. The paper also discusses the results in light of relevant literature and suggests areas for improvement and future research. The authors have made the dataset and Python code files used for the analysis available for further research in the crucial and contemporary field of Indian judicial reform.