PyGuardian

AI Ethics Framework

Overview

The AI Ethics Framework is a comprehensive tool designed to assess the ethical considerations of AI models across multiple principles. This framework evaluates AI systems based on fairness, transparency, accountability, privacy, safety, robustness, and explainability.

Features

• Fairness Assessment: Evaluates demographic parity, equal opportunity, and disparate impact across sensitive attributes.
• Transparency Evaluation: Analyzes model documentation for architecture disclosure, training data description, performance metrics, and limitations.
• Privacy Consideration: Assesses data handling procedures, including data minimization, anonymization techniques, consent mechanisms, and retention policies.
• Robustness Testing: Checks adversarial robustness, out-of-distribution performance, and stability under data perturbations.
• Comprehensive Evaluation: Provides a holistic assessment across all ethical principles.

Installation

pip install ai-ethics-framework

Usage

from pyguardian import AIEthicsFramework

# Initialize the framework
framework = AIEthicsFramework()

# Evaluate your AI model
results = framework.evaluate(
    model=your_model,
    data=your_data,
    sensitive_attributes=["gender", "race"],
    documentation=model_documentation,
    governance_structure=governance_info,
    audit_trail=audit_info,
    data_handling_procedures=data_procedures,
    use_case=model_use_case,
    risk_assessment=risk_info,
    test_data=robustness_test_data,
    explainability_method=explainability_info
)

# Print the results
print(results)

Detailed Framework Structure

The AIEthicsFramework class is the core of PyGuardian. It contains methods for assessing various ethical principles:

Fairness Assessment: The assess_fairness method evaluates the model's fairness across multiple metrics:

• Demographic Parity
• Equal Opportunity
• Disparate Impact

Transparency Evaluation: The assess_transparency method analyzes the model's documentation for:

• Model Architecture Disclosure
• Training Data Description
• Performance Metrics Disclosure
• Limitations Disclosure

Privacy Consideration: The assess_privacy method examines data handling procedures, checking for:

• Data Minimization
• Anonymization Techniques
• Consent Mechanisms
• Data Retention Policies

Robustness Testing: The assess_robustness method tests the model's resilience through:

• Adversarial Robustness Checks
• Out-of-Distribution Performance Evaluation
• Stability Under Data Perturbations

Comprehensive Evaluation: The evaluate method provides a holistic assessment across all ethical principles, combining the results from individual assessments.

Contributing

We welcome contributions to PyGuardian! Here's how you can contribute:

• Fork the repository
• Create your feature branch (git checkout -b feature/AmazingFeature)
• Commit your changes (git commit -m 'Add some AmazingFeature')
• Push to the branch (git push origin feature/AmazingFeature)
• Open a Pull Request

Please ensure your code adheres to our coding standards and include tests for new features.

License

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

PyGuardian is inspired by various ethical guidelines for AI, including those from IEEE, EU, and other organizations. Thanks to all contributors who have helped shape this framework.

Contact

For any questions, feedback, or issues, please:

• Open an issue in this repository
• Contact the maintainers at bcopeland64@gmail.com

Roadmap

We're constantly working to improve PyGuardian. Here are some features we're planning for future releases:

• Integration with popular machine learning frameworks
• GUI for easier interaction with the framework
• Extended documentation and tutorials
• Support for more fairness metrics and robustness tests

Stay tuned for updates!