---
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](https://huggingface.co/datasets/SnehaDeshmukh/IndianBailJudgments-1200)
- **Paper**: [ArXiv:2507.02506](https://arxiv.org/pdf/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:

```bibtex
@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](https://huggingface.co/datasets/SnehaDeshmukh/IndianBailJudgments-1200)  
**Paper**: [ArXiv:2507.02506](https://arxiv.org/pdf/2507.02506)

---

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