Rawalpindi Women University, Code Room Hub (Pvt.) Ltd. Pakistan

An Explainable AI Framework for Prioritizing Judicial Cases Using Legal Text and Case Characteristics                                                                                                                                                           Abstract: The growing volume of judicial cases presents a significant challenge for court systems, where limited resources and increasing case backlogs can affect the timely processing of disputes. In Pakistan, judicial records contain substantial textual and structural information, yet much of this information remains underutilized for data-driven decision support. This study proposes an explainable artificial intelligence framework for analyzing and prioritizing judicial cases based on case characteristics and legal text.The proposed framework combines Natural Language Processing (NLP) with machine learning techniques to extract meaningful features from judicial case records, including case type, legal provisions, case duration, procedural history, and textual characteristics. Rather than predicting judicial outcomes, the system is designed as a decision-support mechanism that identifies cases requiring comparatively greater attention based on predefined, transparent criteria. An explainability layer is incorporated to show which features contributed to a case's priority classification, helping maintain transparency and human oversight. The research will evaluate multiple NLP and machine learning approaches using publicly available or legally accessible judicial case data. Performance will be assessed using classification metrics and explainability measures, while particular attention will be given to dataset limitations, class imbalance, and potential biases. The proposed approach aims to demonstrate how AI can support judicial workload management without replacing judicial discretion or decision-making. The study provides a context-specific framework for responsible AI adoption in the legal domain and explores how explainable machine learning can be adapted to the challenges of judicial data in developing legal systems such as Pakistan.                                                                                                                                                                                                                                                                                   Keywords: Explainable Artificial Intelligence (XAI), Natural Language Processing (NLP), Machine Learning, Legal AI, Judicial Case Prioritization, Legal Text Analysis, Decision Support Systems, Responsible AI, Judicial Data, Pakistan