标题： IBPS：印度保释预测系统 显示英文标题

标题： IBPS: Indian Bail Prediction System

摘要： 保释决定是印度法院中最常审理的事项之一，但仍然受到主观性、延误和不一致性的困扰。 印度监狱人口中超过75%是未审前囚犯，许多来自社会经济地位较低的背景，缺乏及时公正的保释裁决加剧了人权问题，并导致司法系统积压。 在本文中，我们提出了印度保释预测系统（IBPS），这是一个由人工智能驱动的框架，旨在通过仅基于事实案件属性和法律规定来预测结果并生成合法合理的理由，从而协助保释决策。 我们整理并发布了一个包含150,430份高等法院保释判决的大规模数据集，其中包含了结构化注释，如年龄、健康状况、犯罪记录、犯罪类别、羁押时长、法律条款和司法推理。 我们使用参数高效的微调技术对大型语言模型进行微调，并在多种配置下评估其性能，包括有无法律条文上下文以及使用RAG。 我们的结果表明，结合法律知识微调的模型显著优于基线模型，实现了较高的准确性和解释质量，并能很好地泛化到由法律专家独立注释的测试集。 IBPS提供了一个透明、可扩展和可重复的解决方案，以支持数据驱动的法律援助，减少保释延误，并促进印度司法系统的程序公平。 显示英文摘要

摘要： Bail decisions are among the most frequently adjudicated matters in Indian courts, yet they remain plagued by subjectivity, delays, and inconsistencies. With over 75% of India's prison population comprising undertrial prisoners, many from socioeconomically disadvantaged backgrounds, the lack of timely and fair bail adjudication exacerbates human rights concerns and contributes to systemic judicial backlog. In this paper, we present the Indian Bail Prediction System (IBPS), an AI-powered framework designed to assist in bail decision-making by predicting outcomes and generating legally sound rationales based solely on factual case attributes and statutory provisions. We curate and release a large-scale dataset of 150,430 High Court bail judgments, enriched with structured annotations such as age, health, criminal history, crime category, custody duration, statutes, and judicial reasoning. We fine-tune a large language model using parameter-efficient techniques and evaluate its performance across multiple configurations, with and without statutory context, and with RAG. Our results demonstrate that models fine-tuned with statutory knowledge significantly outperform baselines, achieving strong accuracy and explanation quality, and generalize well to a test set independently annotated by legal experts. IBPS offers a transparent, scalable, and reproducible solution to support data-driven legal assistance, reduce bail delays, and promote procedural fairness in the Indian judicial system.