  An automated system that could assist a judge in predicting the outcome of a
case would help expedite the judicial process. For such a system to be
practically useful, predictions by the system should be explainable. To promote
research in developing such a system, we introduce ILDC (Indian Legal Documents
Corpus). ILDC is a large corpus of 35k Indian Supreme Court cases annotated
with original court decisions. A portion of the corpus (a separate test set) is
annotated with gold standard explanations by legal experts. Based on ILDC, we
propose the task of Court Judgment Prediction and Explanation (CJPE). The task
requires an automated system to predict an explainable outcome of a case. We
experiment with a battery of baseline models for case predictions and propose a
hierarchical occlusion based model for explainability. Our best prediction
model has an accuracy of 78% versus 94% for human legal experts, pointing
towards the complexity of the prediction task. The analysis of explanations by
the proposed algorithm reveals a significant difference in the point of view of
the algorithm and legal experts for explaining the judgments, pointing towards
scope for future research.
