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IBPS: Indian Bail Prediction System

Created by
  • Haebom

Author

Puspesh Kumar Srivastava, Uddeshya Raj, Praveen Patel, Shubham Kumar Nigam, Noel Shallum, Arnab Bhattacharya

Outline

This paper highlights the challenges of subjectivity, delay, and inconsistency in bail decisions in Indian courts and proposes the Indian Bail Prediction System (IBPS). IBPS is an AI-based framework that predicts bail decisions based solely on factual case attributes and legal provisions and generates legally sound rationales. We construct a dataset of over 150,000 high court bail decisions and add structural annotations, including age, health status, criminal history, offense type, detention period, legal provisions, and reasons for the decisions. We fine-tune a large-scale language model using parameter-efficient techniques and evaluate its performance in various configurations, including with and without legal provisions and using Retrieval Augmented Generation (RAG). We demonstrate that the model fine-tuned using legal knowledge achieves significantly better accuracy and explanation quality than the baseline model and generalizes well even on a test set independently annotated by legal experts. IBPS offers a transparent, scalable, and reproducible solution that provides data-driven legal assistance, reduces bail delays, and promotes procedural fairness in the Indian judicial system.

Takeaways, Limitations

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Takeaways:
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Presenting the possibility of solving the problems of subjectivity, delay, and inconsistency in gem determination by utilizing AI-based systems.
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Contributing to the advancement of legal AI research by building and releasing large-scale legal datasets.
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We demonstrate that an AI model incorporating legal knowledge is effective in improving gem prediction accuracy and explainability.
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To provide practical solutions that can contribute to improving the efficiency and fairness of the Indian judicial system.
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Limitations:
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Further validation of the model's generalization performance is needed, particularly its applicability to other regions or legal systems.
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Lack of discussion on human intervention and oversight of AI model predictions.
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Lack of analysis of dataset bias and the resulting potential bias in the model.
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AI models may have limitations in fully reflecting the complexities of legal interpretation.
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