Comply · Global standards & industry frameworks
UNESCO Recommendation on AI Ethics
The global standard-setting recommendation on AI ethics.
The UNESCO Recommendation on the Ethics of Artificial Intelligence is a global standard-setting instrument adopted by member states. It centers human rights and dignity and sets out values and principles — including proportionality, safety, fairness, transparency, human oversight, and sustainability — along with concrete policy action areas.
Who it applies to
Member states and organizations seeking an internationally recognized AI-ethics baseline.
Key obligations
- Protect human rights, dignity, and the environment
- Apply proportionality and do-no-harm
- Ensure fairness, transparency, and human oversight
- Support accountability and impact assessment
How Maetra maps agents to UNESCO AI Ethics
Maetra helps operationalize the Recommendation's principles into auditable controls and evidence across the AI lifecycle.
In practice, Maetra:
- Scans and fingerprints each agent. Discover reads the agent’s code — its tools, data access and sensitivity, actions, model, and environment — into an evidence-backed profile tied to the exact file and commit.
- Decides what applies. That profile determines whether UNESCO AI Ethics is in scope for the agent and which of its requirements apply.
- Auto-detects controls and surfaces gaps. Controls your code already satisfies are detected automatically from the scan; the rest become a clear list of gaps, each tied to the requirement and the evidence it still needs.
- Proves it and keeps it current. Close gaps with linked evidence or generated documents — reused across every framework the same control supports — and Maetra re-checks on each rescan and flags evidence that has gone stale.
Related frameworks
Prove UNESCO AI Ethics compliance with Maetra
Classify your AI agents once and Maetra maps them to UNESCO AI Ethics and every other framework it supports — generating the evidence and documentation, tracking gaps and deadlines, and sealing every decision in an immutable audit trail.