Synthetic Content Marking and Disclosure

Mark synthetic outputs where required and disclose deepfake or qualifying public-interest AI-generated content.

Treat provider output marking and deployer publication disclosure as separate tasks.

1. Identify the content types, system functions and publishing routes covered by the relevant Article 50 duty. Record any exception and the conditions supporting it.
2. For providers, implement technically feasible machine-readable marking and detection arrangements appropriate to the content. Test reliability through representative output and handling paths.
3. For deployers, disclose qualifying deepfake content and relevant public-interest text in the appropriate way. Assess artistic-work treatment and human editorial review separately.
4. Record failures, correct the workflow and retest after changes. Do not assume that a visible label meets the machine-readable duty, or that a machine-readable mark alone provides the required disclosure to people. Check applicable transition rules in the linked obligation.

During applicable generation and publication; before workflow release; after material model, format, platform or editorial changes.

Content and publishing-route assessment Marking or provenance configuration and representative test results Approved disclosure examples and placement checks Documented editorial-control or other exception decisions Failure, remediation and retest records

Follow an authorized sample from generation through its intended publication route. Check the required machine-readable marking separately from the human-facing disclosure. Test that relied-on exceptions are documented and do not silently apply to unrelated content.