IBPS: Indian Bail Prediction System
Puspesh Kumar Srivastava, Uddeshya Raj, Praveen Patel, Shubham Kumar Nigam, Noel Shallum, and Arnab Bhattacharya

TL;DR
The paper introduces IBPS, an AI system that predicts bail decisions in India using a large dataset and language models, aiming to improve fairness, reduce delays, and support legal decision-making.
Contribution
It presents a large-scale bail judgment dataset and fine-tunes language models with statutory knowledge for improved bail prediction accuracy.
Findings
Models with statutory context outperform baselines.
High accuracy and explanation quality achieved.
Good generalization to expert-annotated test set.
Abstract
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.…
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Taxonomy
TopicsArtificial Intelligence in Law · Criminal Justice and Corrections Analysis · Law, Economics, and Judicial Systems
