P06 / AI governance / Independent work sample
AI transparency.
AI transparency duties need a documented applicability and evidence review.
The decision
behind the work.
Operate product gates for AI-interaction disclosures, synthetic-output marking, deepfake disclosure, exceptions, and evidence retention.
Methodology and scope are defined in the project manifest. Listed capabilities describe the module design; source files show the implemented subset.
Assessment approach
- Applicability decision tree
- Disclosure UI test
- Machine-readable marking test
- Detector interoperability test
- Exception record
- Release gate
Evidence
to request.
Start with source records, owner confirmation, scope and observation dates. A completed template alone does not establish operating effectiveness.
Defined output contract
- article50-decision.json
- transparency-test-results.json
- release-gate.json
- exception-review.json
The manifest defines these expected artifacts; confirm their existence and completion in source before relying on an output.
Read the full manifest ↗Review & decision boundaries
- Applicability and exceptions require legal review
- Technical marking must be tested, not asserted
- Accessibility is part of disclosure effectiveness
Remediation sequence
Record each finding with its evidence reference, risk rationale, accountable owner, target date and closure test. Escalate missing evidence rather than treating it as a pass.
Present management with the supported conclusion, remaining uncertainty and a specific decision request.