Responsible AI Toolkit

Introducing RAI Toolkit: Practical, Plug-In Tools & Policies for Building AI Responsibly

A practical collection of tools, policies, and checklists to help teams build AI systems responsibly. Born from real-world experience implementing responsible AI practices across different organizations.

⚡ Quick Start

git clone https://github.com/oommensy/rai-toolkit.git
cd rai-toolkit && python tools/pii_scanner.py demo_data/sample_document_with_pii.txt
python tools/toxic_content_evaluator.py --text "This is a sample text to evaluate"

🏗️ What's Inside

graph TD
    A[🧰 RAI Toolkit] --> B[🔧 Security Tools]
    A --> C[📋 Policies & Templates]
    A --> D[⚡ Automation & CI]
    
    B --> B1[🔍 PII Scanner]
    B --> B2[⚖️ Bias Auditor]
    B --> B3[🛡️ Toxicity Evaluator]
    B --> B4[🔒 Prompt Injection Scanner]
    
    C --> C1[📝 AI Use Policy]
    C --> C2[🚀 Model Release Policy]
    C --> C3[📊 Data Governance]
    C --> C4[🚨 Incident Response]
    C --> C5[📄 Model/Data Card Templates]
    
    D --> D1[✅ Pre-commit Hooks]
    D --> D2[🤖 GitHub Actions]
    D --> D3[📋 PR/Deployment Checklists]
    D --> D4[🧪 Evaluation Runner]
    
    style A fill:#4CAF50,stroke:#2E7D32,color:#fff
    style B fill:#2196F3,stroke:#1565C0,color:#fff
    style C fill:#FF9800,stroke:#E65100,color:#fff
    style D fill:#9C27B0,stroke:#6A1B9A,color:#fff

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This toolkit gives you the building blocks that teams actually need:

• Working security scanners for PII, secrets, and prompt injection attacks
• Bias detection tools that catch unfair dataset distributions
• Ready-to-use policies for AI governance, incident response, and model releases
• Documentation templates that pass compliance audits
• CI/CD examples that integrate with your existing workflows

Why Another RAI Toolkit?

Most responsible AI resources are either too academic or too vendor-specific. I built this because:

• Teams kept rebuilding the same basic scanners and policies
• Existing tools didn't integrate well with normal development workflows
• Documentation was either missing or overly complex
• Nobody had good examples of what "responsible AI in production" actually looks like

Key Features

Security & Privacy

• PII scanner with common patterns (emails, SSNs, phone numbers)
• Secrets detection integrated with pre-commit hooks
• Prompt injection pattern matching

Fairness & Bias

• Dataset bias auditing for protected attributes
• Model evaluation harnesses for systematic testing
• Bias reporting templates

Governance & Compliance

• Model and data card generators
• Risk assessment templates
• Incident response playbooks
• Policy templates you can actually customize

Getting Started

🚀 Quick Setup

python3 -m venv rai-toolkit && source rai-toolkit/bin/activate
pip install -r requirements.txt
pre-commit install
pytest -q

🧭 Find Your Path

• New to RAI? → Start with QUICK_START.md for role-based guidance
• Need specific tools? → Use NAVIGATOR.md to find what you need
• Enterprise deployment? → Follow SETUP.md for Docker & production setup
• Integration help? → Check INTEGRATIONS.md for CI/CD examples

📋 Key Resources

• Templates: Model cards, risk registers, and more
• Checklists: PR reviews and deployment safety
• Policies: Governance and incident response
• Examples: Real-world usage samples

Best Practices for Organizations

• Integrate checklists into deployment and review pipelines.
• Use evaluation scripts as part of model validation and monitoring.
• Customize governance templates for your regulatory and risk context.
• Document models and datasets using provided templates.

Contributing

See CONTRIBUTING.md and the PR RAI Checklist.

License

MIT