Predicting delays in Indian lower courts using AutoML and Decision
  Forests
Mohit Bhatnagar Shivraj Huchhanavar
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.