Responsible AI Checklist CLI

A command-line tool to easily add customizable responsible AI checklists to data science, Generative AI, or traditional machine learning projects.

Why am I making this tool public?

This tool helps ensure that ML/AI projects adhere to ethical guidelines and best practices throughout their lifecycle. Delivering AI models responsibly also helps with compliance, data trust, and robustness. In my day-to-day I use this tool to:

Assist in adhering to legal and regulatory standards.
Build trust with users by ensuring transparency and accountability.
Ensure Ai systems are safe and secure from vulnerabilities.

This CLI compliments the RAI Auditor SaaS service in active in user validation, design and development.

Current Features

Generate customizable AI responsibility checklists
Support for various output formats: Markdown (.md), YAML (.yaml), JSON (.json).
You can now generate checklists in YAML and JSON formats, making it easy to integrate into CI/CD pipelines. See also the GitHub Action template in the repo to automate your responsible AI checks.
Customizable checklist sections. You can consider using this for your data privacy compliance needs for example.
Validation of ethical and technical aspects in CI/CD pipelines using YAML or JSON checklists.

Installation

Install the Responsible AI Checklist CLI using pip:

pip install rai-checklist-cli

Note: that PyYAML is a dependency. You may have to install that separately. Known issue.

Usage

The basic syntax for using the CLI is:

rai-checklist [OPTIONS]

Options:

-h, --help: Show help message and exit
-w, --overwrite: Overwrite existing output file
-o, --output PATH: Specify output file path
-f, --format TEXT: Specify output format (md, yaml, json)
-l, --checklist PATH: Path to custom checklist file
--project-type TEXT: Specify project type for validation (default, machine_learning, web_application, etc.)
--config PATH: Path to the configuration file for validation

Examples

Generate a markdown checklist:

rai-checklist -o checklist.md -f md

Generate a YAML checklist:

rai-checklist -o checklist.yaml -f yaml

Generate a JSON checklist:

rai-checklist -o checklist.json -f json

Validate a checklist for a machine learning project:

rai-checklist -o checklist.yaml -f yaml --project-type machine_learning

See also, example notebook for quick EDA use cases.

Integration into CI/CD Pipelines

You can leverage the YAML or JSON output formats to automate responsible AI checks in your CI/CD pipelines, ensuring ethical and performance guidelines are met before deployment.

Example GitHub Action:

Here's how you can use the rai-checklist-cli in GitHub Actions to automatically validate your AI project's responsible AI checklist.

Create a .github/workflows/ai-responsibility-check.yml file with the following content:

name: Responsible AI Checklist CI

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main

jobs:
  responsibility_checklist:
    runs-on: ubuntu-latest

    steps:
    # Step 1: Checkout repository
    - name: Checkout repository
      uses: actions/checkout@v2

    # Step 2: Set up Python environment
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    # Step 3: Install the checklist CLI and dependencies
    - name: Install dependencies
      run: |
        pip install rai-checklist-cli pyyaml

    # Step 4: Generate the Responsible AI Checklist in YAML format
    - name: Generate YAML Checklist
      run: |
        rai-checklist -o responsible_ai_checklist.yaml -f yaml

    # Step 5: Validate the checklist
    - name: Validate Checklist
      run: |
        python -c "
import yaml
with open('responsible_ai_checklist.yaml') as f:
    checklist = yaml.safe_load(f)
    required_sections = ['Ethical considerations', 'Deployment and Monitoring']
    missing_sections = [s for s in required_sections if s not in checklist['sections']]
    if missing_sections:
        print(f'Missing required sections: {missing_sections}')
        exit(1)
    else:
        print('All required sections are present.')
        "

How It Works:

Generate YAML Checklist: The CLI generates a YAML checklist as part of your CI/CD process.
Validate Checklist: The action reads the YAML checklist and ensures that critical sections (like "Ethical considerations" and "Deployment Monitoring") are present. If any section is missing, the pipeline will fail, enforcing responsible AI practices.

Stages

The default checklist includes the following stages of the AI/ML lifecycle:

Project Motivation
Problem Definition
Performance Measurement
LLM-Specific Evaluation Metrics
Ethical Considerations
Roadmap/Timeline
Contacts/Stakeholders
Collaboration
User Research Aspects
End User Definition
End User Testing
Deployment and Monitoring
Continual Improvement

Customization

You can customize the checklist by creating a YAML or JSON file with your desired sections and items. Use the -l or --checklist option to specify your custom checklist file when running the CLI.

For more information on creating custom checklists, please refer to the documentation.

Contributing

Contributions are welcome! Here's how you can contribute to the project:

Fork the repository
Create a new branch (git checkout -b feature/your-feature-name)
Make your changes
Commit your changes (git commit -am 'Add some feature')
Push to the branch (git push origin feature/your-feature-name)
Create a new Pull Request

Please make sure to update tests as appropriate and adhere to the code of conduct.

Acknowledgments

This project was inspired by and builds upon the work of several existing tools and individuals:

Deon by DrivenData: An ethics checklist for data scientists.
CAITI AI Risk Library by ByteanAtom Research: A comprehensive library for AI risk assessment.

Citations

@software{noble2024raichecklist,
  author = {Noble Ackerson}, 
  title = {RAICheckList: A CLI Tool for Generating Responsible AI Checklists}, 
  year = {2024},
  url = {https://github.com/ByteanAtomResearch/rai-checklist-cli/},
  version = {0.6.8}
}

License

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

Note: This project is currently in development. Features and documentation may be incomplete or subject to change.

TODO:

Complete the documentation for custom checklists
Add more examples and use cases
Include frontend-UI (see screenshot)
Set up continuous integration and testing
Add detailed contribution guidelines