An Explainable AI-Based Legal Research Assistant for Precedent Analysis and Judicial Outcome Prediction Using Dense Retrieval and Transformer Models

Abstract

With more than 45 million cases awaiting disposal across Indian courts as of 2024, the judicial system faces an acute need for faster, smarter tools to support legal research. This work introduces an artificial-intelligence-driven legal research assistant tailored to the jurisprudence of the Supreme Court of India. Starting from a case description written in ordinary language, the system executes a four-stage pipeline: first, it performs dense semantic search by encoding the 26,688 judgments of the Indian Legal Documents Corpus (ILDC) with InLegalBERT and indexing roughly 1.1 million resulting text segments through a FAISS IVFFlat structure; second, it applies cross-encoder reranking to narrow the retrieved candidates down to the five precedents judged most semantically relevant; third, it forecasts the judicial outcome across three possible categories— Allowed, Dismissed, or Partly Allowed—via an InLegalBERT classification head paired with SHAP explainability; and fourth, it produces a structured reasoning summary in Issue–Rule–Application–Conclusion (IRAC) form using the Llama 3 8B model served locally through Ollama. By combining semantic search, neural reranking, interpretable outcome prediction, and AIgenerated legal reasoning inside one coherent architecture, the framework helps legal practitioners locate relevant precedents, anticipate probable case outcomes, and streamline the overall research process. Constructed entirely from openly accessible Indian legal resources and transformer-based architectures, the system establishes an extensible base for intelligent legal support within the Indian judiciary