README.md

metadata

title: Indian Bail Judgment Analysis
emoji: 🏛️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit
tags:
  - legal-ai
  - indian-law
  - bail-analysis
  - fairness
  - nlp
  - legal-bert
  - classification

🏛️ Indian Bail Judgment Analysis System

Overview

This system analyzes Indian bail judgments using multiple machine learning approaches to predict bail outcomes and assess fairness in judicial decisions. It's based on the IndianBailJudgments-1200 dataset and implements state-of-the-art NLP models for legal text analysis.

Features

🎯 Bail Outcome Prediction

• LegalBERT Model: Fine-tuned BERT specifically for legal text analysis
• Logistic Regression: Traditional ML approach with TF-IDF features
• Predicts whether bail will be Granted or Rejected
• Provides confidence scores and probability distributions

⚖️ Fairness Analysis

• Multi-model Approach: LegalBERT, Logistic Regression, and XGBoost
• Identifies potential bias in judicial decisions
• Considers demographic factors and regional variations
• Provides fairness assessment with confidence metrics

📊 Model Comparison

• Side-by-side comparison of all models
• Comprehensive analysis across different approaches
• Interactive interface for exploring model behaviors

Dataset

IndianBailJudgments-1200: A comprehensive dataset of 1,200 Indian bail judgments

• Source: HuggingFace Dataset
• Paper: ArXiv:2507.02506
• Coverage: Multiple Indian courts, diverse case types, comprehensive annotations

Models

1. LegalBERT Models

• Base Model: nlpaueb/legal-bert-base-uncased
• Fine-tuning: Specialized for Indian legal context
• Tasks: Bail outcome prediction and fairness analysis

2. Logistic Regression

• Features: TF-IDF vectorization + engineered features
• Preprocessing: StandardScaler normalization
• Performance: Robust baseline with interpretable results

3. XGBoost

• Application: Fairness analysis
• Features: Combined text and metadata features
• Optimization: Hyperparameter tuning for best performance

Usage

1. Input Case Details: Enter case facts, legal issues, and judgment reasoning
2. Select Parameters: Choose crime type, accused gender, and other relevant factors
3. Choose Model: Select from LegalBERT, LogReg, or XGBoost
4. Analyze Results: View predictions with confidence scores and probability distributions

Technical Details

• Text Processing: Tokenization with 512-token limit
• Feature Engineering: TF-IDF, word count, character count, bias indicators
• Preprocessing: StandardScaler for numerical features, LabelEncoder for categories
• Output: JSON format with predictions and confidence metrics

Research Applications

This system supports research in:

• Legal AI: Automated legal decision analysis
• Judicial Fairness: Bias detection in court judgments
• Legal NLP: Natural language processing for legal texts
• Policy Analysis: Understanding patterns in judicial decisions

Limitations

• Models are trained on historical data and may reflect existing biases
• Predictions should be used for research purposes, not legal advice
• Regional and temporal variations may affect accuracy
• Human oversight is essential for critical legal decisions

Citation

If you use this system in your research, please cite:

@article{deshmukh2024indianbail,
  title={Indian Bail Judgment Analysis: A Comprehensive Dataset and Multi-Model Approach},
  author={Deshmukh, Sneha},
  journal={arXiv preprint arXiv:2507.02506},
  year={2024}
}

Contact

Researcher: Sneha Deshmukh
Dataset: SnehaDeshmukh/IndianBailJudgments-1200
Paper: ArXiv:2507.02506

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This system is designed for research and educational purposes. Legal decisions should always involve qualified legal professionals.