whis-ethical-ai 1.0.0

Comprehensive ethical AI validation and auditing package

Ethical AI (eai)

A comprehensive Python package for ethical AI validation and auditing, designed with a modular structure similar to scikit-learn.

Features

• Bias Detection: Identify and measure bias in AI models across different demographic groups
• Fairness Assessment: Evaluate model fairness using various metrics and statistical tests
• GDPR Compliance: Check for data privacy and consent requirements
• AI Act Compliance: Validate compliance with EU AI Act regulations
• Comprehensive Reporting: Generate detailed audit reports with visualizations
• Multiple Model Support: Works with scikit-learn, TensorFlow, and PyTorch models

Installation

From PyPI (Recommended)

pip install whis-ethical-ai

From Source

git clone https://github.com/whis-19/ethical-ai.git
cd ethical-ai
pip install -e .

Quick Start

Basic Usage

from ethical_ai_validator import EthicalAIValidator
import numpy as np
import pandas as pd

# Create sample data
predictions = np.array([1, 0, 1, 0, 1, 0, 1, 1, 0, 1])
true_labels = np.array([1, 0, 1, 1, 0, 0, 1, 1, 0, 1])
protected_attributes = {
    'gender': ['male', 'female', 'male', 'female', 'male', 'female', 'male', 'female', 'male', 'female'],
    'race': ['white', 'black', 'white', 'black', 'white', 'black', 'white', 'black', 'white', 'black']
}

# Initialize validator
validator = EthicalAIValidator()

# Detect bias
bias_report = validator.audit_bias(predictions, true_labels, protected_attributes)
print("Bias Report:")
print(bias_report)

# Calculate fairness metrics
fairness_metrics = validator.calculate_fairness_metrics(predictions, protected_attributes)
print("Fairness Metrics:")
print(fairness_metrics)

Advanced Usage

# Generate compliance report
metadata = {'model_name': 'RandomForest', 'version': '1.0'}
audit_criteria = {'bias_threshold': 0.1, 'fairness_threshold': 0.8}
report_path = validator.generate_compliance_report(metadata, audit_criteria)

# Real-time monitoring
predictions_stream = [
    np.random.choice([0, 1], size=1000),
    np.random.choice([0, 1], size=1000)
]
alerts = validator.monitor_realtime(predictions_stream)

# Suggest mitigations
mitigations = validator.suggest_mitigations(bias_report)
print("Mitigation Suggestions:")
print(mitigations)

Using Convenience Functions

from ethical_ai_validator import (
    audit_bias, calculate_fairness_metrics, generate_compliance_report,
    monitor_realtime, suggest_mitigations
)

# Direct function calls
bias_report = audit_bias(predictions, true_labels, protected_attributes)
fairness_metrics = calculate_fairness_metrics(predictions, protected_attributes)
report_path = generate_compliance_report(metadata, audit_criteria)
alerts = monitor_realtime(predictions_stream)
mitigations = suggest_mitigations(bias_report)

Development Setup

Prerequisites

• Python 3.8 or higher
• Git
• pip

Step-by-Step Setup

1. Clone the repository

   git clone https://github.com/whis-19/ethical-ai.git
   

cd ethical-ai

2. **Create a virtual environment**
```bash
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt
   pip install -e .[dev]
   

4. Run tests

   pytest
   

5. Check code coverage

   pytest --cov=ethical_ai_validator --cov-report=html
   

VS Code Setup

1. Install recommended extensions:

   • Python (ms-python.python)
   • Pylance (ms-python.vscode-pylance)
   • Python Test Explorer (littlefoxteam.vscode-python-test-adapter)

2. Configure settings in .vscode/settings.json (already included)

Project Structure

ethical-ai/
├── src/
│   └── ethical_ai_validator/
│       ├── __init__.py
│       ├── core/
│       ├── validators/
│       ├── metrics/
│       └── reporting/
├── tests/
├── docs/
├── requirements.txt
├── pyproject.toml
├── README.md
└── SETUP.md

Testing

Run the test suite:

# Run all tests
pytest

# Run with coverage
pytest --cov=ethical_ai_validator --cov-report=html

# Run specific test categories
pytest -m unit
pytest -m integration
pytest -m "not slow"

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Code Style

This project uses:

• Black for code formatting
• Flake8 for linting
• MyPy for type checking
• Pre-commit hooks for automated checks

Run pre-commit hooks:

pre-commit install
pre-commit run --all-files

Documentation

• User Guide
• Contributing Guide

License

This project is licensed under the MIT License - see the LICENSE file for complete details.

License Summary

✅ Permitted:

• Commercial use
• Modification and distribution
• Private and public use
• Patent use

❌ Limitations:

• No warranty provided
• No liability for damages

📋 Requirements:

• Include copyright notice
• Include license text
• State any modifications

License Compatibility

The MIT License is compatible with:

• GPL (v2 and v3)
• Apache License 2.0
• BSD Licenses
• Most other open-source licenses

This makes it suitable for use in both open-source and commercial projects.

Third-Party Dependencies

All dependencies are BSD-3-Clause licensed and compatible with MIT:

• numpy, pandas, scikit-learn, reportlab

For detailed license information, see the LICENSE file.

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

Acknowledgments

• Inspired by the need for ethical AI development
• Built with support from the open-source community
• Special thanks to contributors and maintainers (WHIS-19)