Review scope and evidence labels
This page defines AI Transparency as a practical concept for this site. It focuses on how AI-generated, AI-modified or AI-assisted outputs can be disclosed, traced, reviewed and verified.
Evidence labels used on this page:
- Official legal source — based on legal publication sources.
- Standard/specification source — based on public specification material.
- Project/initiative source — based on project or initiative materials.
- Author analysis — interpretation for governance, publication, cybersecurity and compliance on this site.
- Implementation evidence required — local proof needed before a transparency claim is credible.
Concept snapshot
| Field | Value |
|---|---|
| Category | AI disclosure, provenance and accountability |
| Research type | Concept |
| Core idea | A label alone is not enough; transparency requires context and evidence |
| Typical evidence | Disclosure text, provenance metadata, timestamps, signatures, review notes, generation/editing records |
| Related risks | Misleading disclosure, unverifiable claims, synthetic media confusion, responsibility gaps |
| Review status | Official legal and technical provenance sources referenced; local publication workflows require separate implementation evidence |
What AI Transparency means
AI Transparency means making relevant AI involvement understandable and, where necessary, verifiable. Evidence: Official legal source; Author analysis.
For content, this may include whether media was AI-generated, whether text was AI-assisted, whether a human reviewed the output, and whether provenance information can be checked later. Evidence: Author analysis.
For systems and decisions, transparency can include purpose, role of automation, limitations, responsible owner, affected-party information and audit trail. Evidence: Official legal source; Author analysis.
What AI Transparency is not
AI Transparency is not:
- a generic “AI was used” sentence with no context,
- proof that an output is true,
- a substitute for editorial review,
- a guarantee that provenance metadata cannot be stripped,
- a replacement for security controls,
- a replacement for legal assessment in regulated contexts.
Why labels are not enough
A label tells the reader that AI was involved. It does not necessarily show who generated the content, when it was generated, what was modified, who approved it, whether it was reviewed, or whether the label can be verified later. Evidence: Author analysis.
This is why this site treats AI transparency as an evidence problem, not only a visual disclosure problem. Evidence: Author analysis.
Practical transparency layers
| Layer | Purpose |
|---|---|
| Disclosure | Communicate AI involvement to users or readers. |
| Provenance metadata | Preserve origin and editing history where supported. |
| Timestamping | Show when content or evidence was produced. |
| Digital signatures | Support integrity and authenticity checks. |
| Human review | Record editorial or governance responsibility. |
| Audit evidence | Retain proof for later review or dispute. |
Evidence: Standard/specification source; Author analysis.
Relationship to existing Research entries
AI Transparency is related to:
- AI Governance — transparency is one governance control, not the whole governance system,
- Audit Evidence — transparency claims need evidence,
- AI Act — transparency duties may apply depending on context,
- GDPR — personal data, profiling and automated decision-support contexts may require additional information and safeguards,
- Secure AI-Assisted Development — AI-generated software changes also need review and traceability.
Further research directions
Future nodes may cover Content Credentials, watermarking, synthetic media, digital provenance and AI-generated software provenance in more detail.