Bridging Legal Language Barriers Using Explainable AI: Outcome Prediction and Multilingual Knowledge based answer retrieval for Indian Law
TL;DR
An integrated legal AI platform that combines interpretable case outcome prediction with multilingual, retrieval-grounded legal question answering to improve access to Indian law is presented, concluding that transparent machine learning, retrieval-augmented generation, and multilingual interfaces can work together to build a practical and trustworthy legal assistance system for Indian users.
Abstract
This study presents an integrated legal AI platform that combines interpretable case outcome prediction with multilingual, retrieval-grounded legal question answering to improve access to Indian law. The work is motivated by the difficulty ordinary citizens face in understanding legal language, the scarcity of trustworthy guidance, and the need for tools that work across India’s major languages. To address this, the authors built two connected components: a prediction module for Supreme Court case outcomes and a question-answering module based on statutory retrieval and generation. For prediction, they compiled 26,688 Indian Supreme Court judgments from 1950 to 2024 and represented each case using TF-IDF text features, case-type encodings, and temporal metadata, then trained an interpretable logistic regression model. For legal QA, they indexed 21 Indian legal acts with sentence-transformer embeddings and FAISS, and used a locally hosted Mistral model to generate simplified answers grounded in retrieved legal passages. The system was designed for English, Hindi, and Tamil, with translation, speech input, speech output, and interactive visualizations to make legal information more accessible. The prediction model achieved 91.3% accuracy and 0.919 ROC-AUC, while confidence calibration showed a strong correlation between predicted and actual accuracy. In the QA module, the system reached 78.4% precision@5, 86% answer correctness, and only 7% hallucination, a substantial improvement over baseline generative approaches. User evaluation with 35 participants reported 4.05/5 overall satisfaction, with multilingual support and explainability among the most valued features. Overall, the study concludes that transparent machine learning, retrieval-augmented generation, and multilingual interfaces can work together to build a practical and trustworthy legal assistance system for Indian users.