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.

翻译：本文提出一种基于立案时可获取的案件信息预测印度下级法院案件延迟的分类模型。该模型基于420万件于2010年立案、历时10年审结的法院案件数据集构建，数据覆盖印度7000余个下级法院。研究者采用AutoML开发了涵盖所有待审期别的多分类模型，继而运用二元决策森林分类器提升延迟分类的预测精度。最佳模型达到81.4%的准确率，精确率、召回率与F1值均为0.81。研究表明，基于管辖权、法院、法官、案件标的及当事人等关键数据点，AI模型能够有效预测印度法院案件延迟。本文亦结合相关文献对研究结果进行讨论，并提出改进方向与未来研究建议。为促进印度司法改革这一关键前沿领域的研究，作者已公开分析所用的数据集与Python代码文件。