AI governance can feel overwhelming fast: laws, standards, risk frameworks, model documentation, vendor due diligence, and 'what do we do first?' questions from every direction.
A clean starting point is the OECD AI Principles—a widely recognized, values-based set of principles that governments adopted in May 2019 as part of the Organisation for Economic Co-operation and Development (OECD) Recommendation on Artificial Intelligence. They have also influenced other international efforts, including the G20 AI Principles.
For organizations, the OECD AI Principles are useful because they translate well into real governance controls: documented decisions, clear accountability, measurable testing, transparency practices, and ongoing monitoring without forcing you into one specific regulatory regime.
What the OECD Principles Mean in Practice
There are five OECD AI principles. They are easy to recite, but what do they mean in practice? Below we translate those principles into a practical roadmap.
(1) Inclusive growth, sustainable development, and well-being
Principle: AI should benefit people and the planet.
What it looks like in a real program:
- A documented purpose statement: why the AI exists, who benefits, and how you will measure success.
- Consideration of foreseeable harms (to users, employees, customers, or impacted groups) and how you will mitigate them.
- A way to track real-world outcomes, not just model metrics (accuracy/F1 score/etc.).
Example: A predictive maintenance model is launched with clear benefit metrics (e.g., downtime reduction) and a simple outcome review that checks whether it also creates unintended harms (e.g., unsafe workarounds, overtime pressure, energy waste).
Simple artifacts to produce:
- Use case brief, which highlight the intended benefits
- Stakeholder and impact review notes
- Post-launch monitoring plan (Key Performance Indicators (KPIs), thresholds, escalation)
(2) Human-centered values, fairness, human rights, and rule of law
Principle: AI should respect human rights, democratic values, and the rule of law, with safeguards and the ability for humans to intervene when needed.
What it looks like in a real program:
- A clear compliance story for privacy and data protection (lawful basis, minimization, retention, access controls).
- Fairness testing appropriate to the context (e.g., disparate impact in hiring, lending, or healthcare).
- Defined human oversight: who can override decisions, when, and with what authority.
- A plan for user rights and redress, including complaints and remediation.
Example: An AI‑assisted hiring screen is paired with a documented lawful basis and data minimization, fairness tests that match the context, clear human override rules, and an appeal channel for candidates.
Simple artifacts to produce:
- AI and privacy impact assessment (or combined assessment)
- Fairness testing approach, results, and mitigations
- Human-in-the-loop policy (including “when to stop the system”)
(3) Transparency and explainability
Principle: AI actors should provide meaningful transparency and responsible disclosure.
What it looks like in a real program:
- Users are informed when they are interacting with AI (and what the AI is doing).
- Decision-making systems provide appropriate explanations for the audience (customers vs. regulators vs. internal QA).
- Strong internal documentation: system descriptions, limitations, and intended use.
Example: A customer-support chatbot discloses it is AI, offers an easy “talk to a human” option, and has a short internal system card describing training sources, limitations, and known failure cases.
Simple artifacts to produce:
- User notices and disclosures (product UI and policies)
- Model/system documentation (model card, system card, or similar)
- Logging and auditability plan (what you log, why, and retention)
(4) Robustness, security, and safety
Principle: AI systems should be robust and secure across their lifecycle, with risk management and safeguards.
What it looks like in a real program:
- Risk assessment aligned to system criticality and your organization's risk appetite.
- Testing for accuracy, robustness, and known failure modes, repeated when models change.
- Security controls for modern threats (e.g., prompt injection, data leakage, model extraction, poisoned data pipelines).
- Monitoring in production for drift, abuse, and safety incidents.
Example: A forecasting system is re validated whenever it is retrained, monitored for drift, and, if it uses LLM components, tested for prompt injection, data leakage, and unsafe outputs with mitigations documented.
Simple artifacts to produce:
- AI risk assessment and test plan
- Security threat model and mitigations
- Incident response plan (including escalation and notification triggers)
(5) Accountability
Principle: Organizations should be accountable for the proper functioning of AI systems and for compliance with these principles.
What it looks like in a real program:
- Defined roles: product owner, model owner, risk/compliance, privacy, security, legal.
- A formal approval workflow for higher-risk uses.
- Audit-ready evidence: what decisions were made, why, and who approved them.
- Continuous improvement based on incidents, user feedback, monitoring, and changes in law/standards.
Example: High‑risk AI use cases cannot go live without sign‑off from a named owner plus risk/compliance; decisions and evidence are logged in a central register so audits don’t turn into “memory games.”
Simple artifacts to produce:
- Governance charter and RACI
- Approval workflow and sign-off records
- Governance KPIs (incidents, overrides, complaints, drift, testing cadence)
How to Use the OECD AI Principles as a Governance Framework
If you are building (or formalizing) an AI governance program, here is a simple way to apply the principles quickly:
- 1. Pick a scope: one AI system, a product line, or the whole organization.
- 2. Run a lightweight self-assessment against the five principles.
- 3. Collect evidence (existing policies, test results, logs, vendor docs, DPIAs).
- 4. Assign owners and target dates for gaps.
- 5. Track progress regularly (or per release cycle for fast-moving systems).
The OECD AI Principles Assessment Template Explained
This companion template maps each OECD principle to practical governance questions, with scoring and a dashboard. You can use it for baselining, audit readiness, and building a governance roadmap.
Suggested use cases:
- Program baselining (“where are we today?”)
- Evidence tracking (“what proof do we have?”)
- Roadmapping (“what do we fix next quarter?”)
Also check out the FREE OECD AI Principles Strategy Scoredcard