Methodology and accountability
How Disclara produces regulatory interpretation
Disclara turns primary sources into bounded, testable disclosure guidance. A source is identified and dated, its authority is classified, the rule is separated from platform and client policy, uncertainty remains visible, and a reviewer is attributed before the interpretation is presented as reviewed.
Editorial production
From source to published interpretation
Define the question
Start with a bounded asset, AI operation, market, audience and destination instead of a generic compliance claim.
Collect primary sources
Use legislation, regulator guidance, official platform policy, technical standards and first-party product documentation.
Classify authority and time
Record source type, effective date, last-checked date, status and the reviewed snapshot marker.
Compare source versions
Compare simultaneous web, PDF, API and dated editions; preserve format, retrieval time and content digest.
Extract the decision rule
Translate the source into explicit triggers, exceptions, required actions and uncertainty without silently broadening it.
Separate decision layers
Keep binding law, platform policy, client policy, provenance evidence and recommended wording independently visible.
Review and publish
Check the interpretation against its sources, attribute the reviewer and publish the page with a dated changelog trail.
Source hierarchy
What evidence is preferred
| Source type | Use in Disclara | Boundary |
|---|---|---|
| Binding law | Defines statutory duties, scope, dates and exceptions. | Quoted and interpreted within the stated jurisdiction. |
| Regulator or Commission guidance | Explains implementation and official interpretation. | Not silently presented as legislation. |
| Platform policy | Defines native labels, enforcement and publishing workflow. | Kept separate from legal obligation. |
| Technical standard | Supports provenance and implementation guidance. | Does not prove truth or legal compliance. |
| First-party product documentation | Supports tool and export-capability claims. | Capability is not assumed for every plan, format or account. |
| Secondary reporting | May document rollout evidence when first-party detail is incomplete. | Marked as secondary and not used to override a primary source. |
Reviewer attribution
Name, role, review date and verified qualifications
Every important guidance page shows
- The person’s name or an honest organizational byline.
- The role performed in the review.
- The date that page was reviewed.
- A link back to this review standard.
Qualifications are never inferred
Legal, regulatory or technical qualifications are displayed only after the reviewer’s identity, consent and relevant credential have been verified. “Disclara Editorial” is an organizational attribution and does not imply review by a lawyer.
Uncertainty handling
No automatic certainty from incomplete facts
- Missing facts remain missing; the model does not invent an asset, audience, policy or consent state.
- Rules are selected against the planned publication date, with scheduled changes surfaced across the campaign window.
- A future policy announcement creates a recheck gate; it is not treated as if the final rule text were already known.
- Legal obligation, platform action, client rule, provenance and publication readiness remain separate outputs.
- Ambiguous, unsupported or rights-sensitive cases are routed to “Human review required”.
- Materially conflicting versions from the same authority suspend automatic interpretation; no version is silently selected.
- An absent platform AI label is absence of evidence, not evidence that the asset is human-made.
- A missing C2PA credential is not treated as proof that content is human-made.
- A valid credential is not treated as a complete audience-facing description: component-level AI use and whole-asset labels are compared for semantic overstatement.
- Disclosure recommendations follow materiality and aim for the minimum necessary, defensible wording rather than labeling every AI-assisted production step.
- No result is described as guaranteed compliance or legal approval.
AI-assisted editorial work
AI may assist production, but does not own approval
AI tools may assist with source discovery, comparison, drafting, structured-data preparation or testing. An AI-generated draft is not, by itself, a reviewed legal interpretation. The public attribution identifies the review owner responsible for checking the sources and boundaries represented on the page.
Freshness protocol
What happens after a material policy change
- Update the structured source record and its effective or last-checked date.
- Snapshot every material source surface separately and compare its content digest with parallel web, PDF or API versions.
- Version the affected rule with explicit
effective_fromandeffective_toboundaries. - Update every materially affected page and regression fixture where the decision changes.
- Publish a changelog entry linking the source and updated pages.
- Add a short analysis when the change is broad, ambiguous or operationally significant.
Corrections
How to challenge an interpretation
Send the page URL, disputed statement and supporting primary source to hello@disclara.eu. A substantive correction should update the page, its review date and the changelog. Disclara does not silently erase a material interpretation change.
Effective . This policy describes Disclara’s editorial standard; it is not a certification or legal-services engagement.