OECD AI Principles
OECD Principles on Artificial Intelligence
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Framework Domains (15)
Accountability
| Code | Title |
|---|---|
| OECD-AI-REDRESS | Mechanisms for Redress and Contestation |
| OECD-AI-REPORT | Public Reporting and Accountability to Society |
Cross-Border
| Code | Title |
|---|---|
| OECD-AI-EXPORT | Cross-Border Considerations and Interoperability |
Data Governance
| Code | Title |
|---|---|
| OECD-AI-DATAGOV | Data Governance Underpinning Trustworthy AI |
Implementation
| Code | Title |
|---|---|
| OECD-AI-DEFN | Common Understanding of AI System and AI Actor Definitions |
| OECD-AI-LIFECYCLE | Lifecycle Coverage of AI Principles |
| OECD-AI-MULTI-STAKEHOLDER | Multi-Stakeholder Engagement |
Lifecycle
| Code | Title |
|---|---|
| OECD-AI-CHANGE | Change Management and Reassessment |
Measurement
| Code | Title |
|---|---|
| OECD-AI-METRICS | Metrics and Indicators for Trustworthy AI |
OECD AI Principles: AI Accountability & Oversight
Human oversight and accountability for AI (OECD AI Principles)
| Code | Title |
|---|---|
| OECD-AI-16 | Human oversight mechanisms |
| OECD-AI-17 | Accountability framework for AI systems |
| OECD-AI-18 | AI incident reporting and response |
| OECD-AI-19 | Regulatory compliance for AI |
| OECD-AI-20 | Third-party AI audit requirements |
OECD AI Principles: AI Data Governance
Governing data used in AI systems (OECD AI Principles)
| Code | Title |
|---|---|
| OECD-AI-11 | Training data quality and governance |
| OECD-AI-12 | Data bias assessment and mitigation |
| OECD-AI-13 | Data provenance and lineage tracking |
| OECD-AI-14 | Privacy protection in AI training data |
| OECD-AI-15 | Data retention for AI models |
OECD AI Principles: AI Risk Management
Managing risks associated with AI systems (OECD AI Principles)
| Code | Title |
|---|---|
| OECD-AI-01 | AI risk identification and assessment |
| OECD-AI-02 | AI system categorization by risk level |
| OECD-AI-03 | Bias detection and mitigation |
| OECD-AI-04 | AI model validation and testing |
| OECD-AI-05 | Ongoing AI risk monitoring |
OECD AI Principles: AI Safety & Security
Ensuring AI system safety and security (OECD AI Principles)
| Code | Title |
|---|---|
| OECD-AI-21 | AI system robustness and resilience |
| OECD-AI-22 | Adversarial attack protection |
| OECD-AI-23 | AI model security and integrity |
| OECD-AI-24 | Safe AI deployment procedures |
| OECD-AI-25 | AI system lifecycle management |
OECD AI Principles: AI Transparency & Explainability
Ensuring transparency in AI decision-making (OECD AI Principles)
| Code | Title |
|---|---|
| OECD-AI-06 | AI system documentation requirements |
| OECD-AI-07 | Algorithmic transparency measures |
| OECD-AI-08 | Explainability requirements for high-risk AI |
| OECD-AI-09 | User notification of AI interactions |
| OECD-AI-10 | Record-keeping for AI decisions |
Recommendations to Policymakers
| Code | Title |
|---|---|
| OECD-AI-2.1 | Investing in AI Research and Development |
| OECD-AI-2.2 | Fostering a Digital Ecosystem for AI |
| OECD-AI-2.3 | Shaping an Enabling Policy Environment for AI |
| OECD-AI-2.4 | Building Human Capacity and Preparing for Labour Market Transformation |
| OECD-AI-2.5 | International Cooperation for Trustworthy AI |
Risk Management
| Code | Title |
|---|---|
| OECD-AI-IMPACT | Impact Assessments Aligned to OECD Principles |
Transparency
| Code | Title |
|---|---|
| OECD-AI-DISCLOSURE | Public-Facing Disclosure of AI Use |
Values-Based Principles
| Code | Title |
|---|---|
| OECD-AI-1.1 | Inclusive Growth, Sustainable Development, and Well-Being |
| OECD-AI-1.2 | Human-Centred Values and Fairness |
| OECD-AI-1.3 | Transparency and Explainability |
| OECD-AI-1.4 | Robustness, Security, and Safety |
| OECD-AI-1.5 | Accountability |
Your Compliance Coverage
If you comply with OECD AI Principles, you already cover:
EU AI Act
22%
10 controls mapped
Compare →ISO 42001
22%
10 controls mapped
Compare →China AI Regulations
22%
10 controls mapped
Compare →+ 596 more: NIST AI Risk Management Framework (AI RMF 1.0) (22%), NIST AI 600-1 Generative AI Profile (22%)
See all 599 mapped frameworks ↓Maps to 599 other frameworks
Frequently Asked Questions
What is OECD AI Principles?
OECD AI Principles is a compliance framework from International with 15 domains and 46 controls. OECD Principles on Artificial Intelligence It is used by organisations to establish and maintain compliance with industry standards and regulatory requirements.
How many controls does OECD AI Principles have?
OECD AI Principles has 46 controls organised across 15 domains. The largest domains are OECD AI Principles: AI Accountability & Oversight (5 controls), OECD AI Principles: AI Data Governance (5 controls), OECD AI Principles: AI Risk Management (5 controls). Each control defines specific requirements that organisations must implement to achieve compliance.
What frameworks does OECD AI Principles map to?
OECD AI Principles maps to 599 other compliance frameworks. The top mapping partners are EU AI Act (22% coverage), ISO 42001 (22% coverage), China AI Regulations (22% coverage). Use our comparison tool to explore control-level mappings between frameworks.
How do I get started with OECD AI Principles compliance?
Start your OECD AI Principles compliance journey by running a self-assessment on our platform to identify your current compliance posture. Our AI advisory can answer specific questions about OECD AI Principles requirements, and cross-framework mapping helps you leverage existing controls from other frameworks you may already comply with. Create a free account to access all 46 controls and track your progress.
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