Learning Ethical AI

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🛡️ Ethical AI: The 2026 Resource Guide

This guide provides essential technical and regulatory updates for developers and AI practitioners building in the Generative & Agentic Era (2024–2026).

📂 Repository Structure

learning-ethical-ai/
│
├── 01-tools/                    # AI safety and ethics tools
│   ├── README.md                  # Tool comparison matrix, quick start
│   ├── 01-giskard/                   # LLM testing & vulnerability scanning
│   │   ├── README.md
│   │   ├── config_vertexai.py     # GCP Vertex AI configuration
│   │   └── healthcare_scan.py     # Working healthcare LLM audit
│   ├── 02-nemo-guardrails/          # Runtime safety controls
│   │   ├── README.md
│   │   └── healthcare_rails/      # Production-ready clinical guardrails
│   ├── 03-model-cards/              # Model documentation & transparency
│   │   └── README.md
│   └── 04-llama-guard/              # Content safety classification
│       └── README.md
│
├── 02-examples/                 # Jupyter notebooks (6 complete examples)
│   ├── README.md
│   ├── requirements.txt
│   ├── 01-giskard-quickstart.ipynb
│   ├── 02-llm-hallucination-detection.ipynb
│   ├── 03-healthcare-llm-safety.ipynb
│   ├── 04-clinical-guardrails.ipynb
│   ├── 05-mcp-security-audit.ipynb
│   └── 06-agent-ethics-patterns.ipynb
│
├── 04-healthcare/               # Healthcare-specific AI ethics
│   ├── clinical-llm-risks.md      # EHR integration risks, hallucinations
│   ├── hipaa-ai-checklist.md      # HIPAA compliance for AI
│   ├── genomics-ethics.md         # Ethical AI in genetic analysis
│   ├── who-lmm-guidelines.md      # WHO 2025 LMM guidance summary
│   └── synthetic-patient-data.md  # Safe synthetic data generation
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├── 05-agentic-safety/           # MCP and agentic AI security
│   ├── mcp-security-threats.md    # OWASP-style MCP threat taxonomy
│   ├── safe-mcp-patterns.md       # OpenSSF Safe-MCP security patterns
│   ├── human-in-loop-agents.md    # HITL design for high-risk actions
│   ├── tool-poisoning-defense.md  # Defense strategies
│   └── audit-logging-agents.md    # Agent decision chain tracing
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├── 06-governance/               # Regulatory compliance resources
│   ├── eu-ai-act-checklist.md     # High-risk system requirements
│   ├── nist-ai-600-1-summary.md   # GenAI risk profile summary
│   └── risk-tiering-template.md   # AI system risk classification
│
└── README.md                    # This file

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🚀 Quick Start

Install Dependencies

# Clone repository
git clone https://github.com/lynnlangit/learning-ethical-ai.git
cd learning-ethical-ai

# Install tools
pip install giskard nemoguardrails model-card-toolkit

# Configure GCP (required for examples)
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-key.json"
export GCP_PROJECT_ID="your-project-id"
export GCP_REGION="us-central1"

Run Your First Safety Scan

cd 01-tools/giskard
python healthcare_scan.py
# Opens HTML report with safety analysis

Explore Jupyter Notebooks

cd 02-examples
pip install -r requirements.txt
jupyter notebook
# Start with 01-giskard-quickstart.ipynb

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📚 Documentation Index

Topic	Description	Link
🎓 Learning Paths	Step-by-step guides for different roles (Beginner, Dev, Security, Compliance)	Start Learning →
🧪 Tools	Giskard, NeMo Guardrails, Wallarm, Model Cards setup	View Tools →
🧬 Healthcare	WHO guidelines, HIPAA, Genomics, Clinical Risks	View Healthcare →
🤖 Agentic Safety	MCP Security, Threats, HITL, Tool Poisoning	View Agent Security →
🏛️ Governance	EU AI Act, NIST, US Courts, State Laws	View Governance →

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✅ Developer "Ethics-by-Design" Checklist

Before deploying your AI system:

• Risk Tiering: Classify your system using 06-governance/risk-tiering-template.md
• Safety Testing: Run Giskard comprehensive scan (see 01-tools/01-giskard/)
• Guardrails: Implement NeMo Guardrails for runtime safety (see 01-tools/02-nemo-guardrails/)
• Compliance: Review EU AI Act requirements if deploying in EU (see 06-governance/eu-ai-act-checklist.md)
• Legal/Courts: Check US Court AI Rules if building legal tech (see 06-governance/us-court-ai-justice.md)
• Healthcare: If clinical use, check HIPAA compliance (see 04-healthcare/hipaa-ai-checklist.md)
• Agentic: If using MCP, audit security (see 05-agentic-safety/mcp-security-threats.md)
• Human Oversight: Implement HITL for high-risk actions (see 05-agentic-safety/human-in-loop-agents.md)
• Documentation: Create Model Card (see 01-tools/03-model-cards/)
• Audit Logging: Enable comprehensive logging (see 05-agentic-safety/audit-logging-agents.md)

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🔗 Key Resources

Official Guidelines

• EU AI Act
• NIST AI RMF
• WHO LMM Guidance
• OECD AI Principles

Tools & Frameworks

• Giskard - LLM testing
• NeMo Guardrails - Runtime safety
• OpenSSF Safe-MCP - MCP security
• Model Cards Toolkit

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📝 License

MIT License - See LICENSE file for details

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👤 Author

Lynn Langit

• Background: Mayo Clinic / Genomics
• Focus: Healthcare AI ethics, cloud architecture, precision medicine
• GitHub: @lynnlangit

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💬 Chat with this Repo (NotebookLM)

You can use Google's NotebookLM to turn this repository into an interactive expert that answers your questions.

1. Go to NotebookLM.
2. Create a new notebook.
3. Click Add Source > GitHub (or paste the repo URL: https://github.com/lynnlangit/learning-ethical-ai).
4. Select this repository.

Try asking:

• "What are the new HIPAA requirements for AI?"
• "Summarize the MCP security threats."
• "Create a checklist for EU AI Act compliance."
• "Listen to the Audio Overview for a podcast-style summary."

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🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

For major changes, please open an issue first to discuss proposed changes.

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Last Updated: January 2026 Status: Active development - Repository reflects current 2026 standards for ethical AI