@inproceedings{raj-etal-2026-ibps,
title = "{IBPS}: {I}ndian Bail Prediction System",
author = "Raj, Uddeshya and
Srivastava, Puspesh Kumar and
Patel, Praveen and
Nigam, Shubham Kumar and
Shallum, Noel and
Bhattacharya, Arnab",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.63/",
pages = "1236--1260",
ISBN = "979-8-89176-444-6",
abstract = "Bail decisions constitute a large share of criminal proceedings in Indian courts, yet they often suffer from delays, inconsistency, and limited transparency. More than 75{\%} of India{'}s prison population consists of undertrial prisoners, highlighting the urgency of improving the efficiency of bail adjudication. In this work, we present the Indian Bail Prediction System (IBPS), an AI-assisted framework that predicts bail outcomes and generates legally grounded explanations from factual case attributes and statutory context. We construct a large-scale dataset of 89,969 bail judgments from 5 High Courts of India, enriched with structured annotations such as statutes, custody duration, criminal history, health conditions, and judicial reasoning. Using this dataset, we design a two-stage architecture that combines a transformer-based classifier for outcome prediction with a fine-tuned large language model for rationale generation. Our results demonstrate the feasibility of combining structured legal data and large language models to develop interpretable AI systems that support data-driven analysis of bail decisions."
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<abstract>Bail decisions constitute a large share of criminal proceedings in Indian courts, yet they often suffer from delays, inconsistency, and limited transparency. More than 75% of India’s prison population consists of undertrial prisoners, highlighting the urgency of improving the efficiency of bail adjudication. In this work, we present the Indian Bail Prediction System (IBPS), an AI-assisted framework that predicts bail outcomes and generates legally grounded explanations from factual case attributes and statutory context. We construct a large-scale dataset of 89,969 bail judgments from 5 High Courts of India, enriched with structured annotations such as statutes, custody duration, criminal history, health conditions, and judicial reasoning. Using this dataset, we design a two-stage architecture that combines a transformer-based classifier for outcome prediction with a fine-tuned large language model for rationale generation. Our results demonstrate the feasibility of combining structured legal data and large language models to develop interpretable AI systems that support data-driven analysis of bail decisions.</abstract>
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%0 Conference Proceedings
%T IBPS: Indian Bail Prediction System
%A Raj, Uddeshya
%A Srivastava, Puspesh Kumar
%A Patel, Praveen
%A Nigam, Shubham Kumar
%A Shallum, Noel
%A Bhattacharya, Arnab
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F raj-etal-2026-ibps
%X Bail decisions constitute a large share of criminal proceedings in Indian courts, yet they often suffer from delays, inconsistency, and limited transparency. More than 75% of India’s prison population consists of undertrial prisoners, highlighting the urgency of improving the efficiency of bail adjudication. In this work, we present the Indian Bail Prediction System (IBPS), an AI-assisted framework that predicts bail outcomes and generates legally grounded explanations from factual case attributes and statutory context. We construct a large-scale dataset of 89,969 bail judgments from 5 High Courts of India, enriched with structured annotations such as statutes, custody duration, criminal history, health conditions, and judicial reasoning. Using this dataset, we design a two-stage architecture that combines a transformer-based classifier for outcome prediction with a fine-tuned large language model for rationale generation. Our results demonstrate the feasibility of combining structured legal data and large language models to develop interpretable AI systems that support data-driven analysis of bail decisions.
%U https://aclanthology.org/2026.findings-aacl.63/
%P 1236-1260
Markdown (Informal)
[IBPS: Indian Bail Prediction System](https://aclanthology.org/2026.findings-aacl.63/) (Raj et al., Findings 2026)
ACL
- Uddeshya Raj, Puspesh Kumar Srivastava, Praveen Patel, Shubham Kumar Nigam, Noel Shallum, and Arnab Bhattacharya. 2026. IBPS: Indian Bail Prediction System. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 1236–1260, Hengqin, China. Association for Computational Linguistics.