Other Frameworks
Additional international principles, assessment tools, and sector-specific frameworks relevant to AI governance.
International Principles Frameworks
OECD AI Principles (Updated May 2024)
The most widely referenced intergovernmental AI principles. Key emphases: human rights, transparency, robustness, security, safety, and accountability. Used as a normative foundation by many national AI strategies.
Use in the toolkit: Inform your council's Principles and external commitments.
UNESCO Recommendation on the Ethics of AI
Applies across all 194 UNESCO member states. Places human rights, dignity, fairness, transparency, and human oversight at the center.
Use in the toolkit: Strengthens the human-rights and fairness dimensions of Impact Assessments.
Council of Europe Framework Convention on AI
The first legally binding international treaty on AI. Designed to align AI with human rights, democracy, and the rule of law.
Use in the toolkit: Relevant for organizations operating in Council of Europe member states. Informs Principles and compliance commitments.
Assessment Tools
Canada: Algorithmic Impact Assessment (AIA)
A mandatory questionnaire tool for federal automated decision systems. Uses 65 risk questions and 41 mitigation questions to determine impact level.
Use in the toolkit: A model for structured intake and risk assessment. Informs Use Case Registration and Risk Tiering.
NSW: AI Assessment Framework (AIAF)
A risk self-assessment framework covering AI design, development, deployment, procurement, and use.
Use in the toolkit: Informs the Impact Assessment template and Vendor Checklist.
Queensland: FAIRA
A transparency, accountability, and risk-identification tool for agencies evaluating AI solutions.
Use in the toolkit: Informs intake form design and accountability documentation.
Singapore: AI Verify
A testing framework and toolkit aligned to 11 internationally recognized AI governance principles. NIST hosts crosswalks between AI Verify and AI RMF.
Use in the toolkit: Informs the Review & Assurance Pack testing approach.
Sector-Specific Resources
| Sector | Resource | Focus |
|---|---|---|
| Healthcare | AMA AI Governance Toolkit | Clinical AI governance for health systems |
| Finance | Various national regulators | Model risk management (SR 11-7 in the US) |
| Government | UK Government AI Playbook | Public-sector AI implementation guidance |
| Board-level | IoD Ireland AI Governance Toolkit | Board-facing AI governance for directors |
Security Frameworks
| Framework | Focus |
|---|---|
| Google SAIF | Six core elements for securing AI systems |
| OWASP Top 10 for LLMs | Practical risks for large language model applications |
| NIST SP 800-218A | AI-specific secure software development practices |
See Security Review for how these are used in the toolkit.