Exploring Graph Neural Networks for Indian Legal Judgment Prediction
Mann Khatri Mirza Yusuf Yaman Kumar Rajiv Ratn Shah Ponnurangam Kumaraguru
The burdensome impact of a skewed judges-to-cases ratio on the judicial
system manifests in an overwhelming backlog of pending cases alongside an
ongoing influx of new ones. To tackle this issue and expedite the judicial
process, the proposition of an automated system capable of suggesting case
outcomes based on factual evidence and precedent from past cases gains
significance. This research paper centres on developing a graph neural
network-based model to address the Legal Judgment Prediction (LJP) problem,
recognizing the intrinsic graph structure of judicial cases and making it a
binary node classification problem. We explored various embeddings as model
features, while nodes such as time nodes and judicial acts were added and
pruned to evaluate the model's performance. The study is done while considering
the ethical dimension of fairness in these predictions, considering gender and
name biases. A link prediction task is also conducted to assess the model's
proficiency in anticipating connections between two specified nodes. By
harnessing the capabilities of graph neural networks and incorporating fairness
analyses, this research aims to contribute insights towards streamlining the
adjudication process, enhancing judicial efficiency, and fostering a more
equitable legal landscape, ultimately alleviating the strain imposed by
mounting case backlogs. Our best-performing model with XLNet pre-trained
embeddings as its features gives the macro F1 score of 75% for the LJP task.
For link prediction, the same set of features is the best performing giving ROC
of more than 80%