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An AI-assisted explainer is only as trustworthy as the link between what it says on screen and the source behind each statement. A source-to-scene map is a simple ledger that records every important factual claim, the source that supports it, and the exact scene, narration line, chart, or caption where the claim appears. Building one lets a reviewer check a script line by line, lets a revision trigger the right re-check, and lets you explain your AI use to viewers without overstating what that explanation proves.
What the map is and what it is not
The map is an editorial method, not a regulatory or technical standard. The official guidance on AI-generated content that this approach draws on does not prescribe a template, so the structure below is a practical synthesis rather than a required format. Google Search Central, for example, says AI-generated content should be manually fact-checked and reviewed before publishing, and that sharing how content was created can give readers useful context. It does not tell you how to organize that checking.
The map answers one question for every factual statement in an explainer: where did this come from, and who checked it against the original?
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- Break the explainer into scenes or beats. Number each one. A beat can be a narration block, a single animated graphic, or an on-screen title card.
- List every factual claim in each scene. Include claims made in narration, captions, chart labels, lower thirds, and figures shown in graphics. Claims hidden in visuals are the ones most often missed.
- Record the ledger fields for each claim (see the table below).
- Label the type of claim. Mark it as directly stated by a source, as a calculation you performed from source figures, or as an editorial inference. Each label carries a different burden of proof.
- Route each entry to a human reviewer who opens the cited source, confirms the passage says what the script says, and confirms the visual does not suggest more than the source supports.
- Re-check on every change. When a scene is edited, the entries linked to it go back into review.
The fields each ledger entry needs
A ledger entry is only useful if a reviewer can find the same passage again months later. Record these fields for every claim:
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| Field | What to record | Illustrative entry |
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| Scene reference | Scene number and the asset where the claim appears | Scene 4, narration, 0:42–0:51 |
| Exact wording | The claim as the audience will hear or read it | “The transparency rules apply from 2 August 2026.” |
| Source title and URL | The primary document, not a secondary summary, where one exists | The publisher’s own guidance page, with its URL copied exactly |
| Relevant passage or data | The sentence, table row, or dataset cell that supports the claim | The paragraph stating the application date, saved as a quote |
| Source date | Publication date and last-updated date shown by the source | Date the reviewer opened the page, plus the page’s own date |
| Claim type | Directly stated, calculation, or editorial inference | Directly stated |
| Reviewer and check date | Who verified the entry and when | Reviewer initials, date |
The “relevant passage” field matters most. A link to a homepage or a long report does not let anyone confirm a claim quickly. Saving the exact sentence or data cell turns a vague citation into something a reviewer can check in seconds.
Handling calculations and inferences differently
Not every on-screen statement is a direct quotation, and the ledger should show the difference.
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- Directly stated claims need the exact passage and source date.
- Calculations need the input figures, each with its own source entry, and the formula or arithmetic written out in the ledger. A chart built from those figures inherits the same entries.
- Editorial inferences need a clear statement that they are your reading of the evidence, along with the sources that support the reasoning. A viewer should be able to tell an inference from a sourced fact.
Keep provenance and verification as separate checks
Two different questions get confused in AI workflows. Provenance asks where a piece of content came from, and it may help indicate origin. Claim verification asks whether a specific factual statement is supported by a source. A watermark, a metadata tag, or a platform label can answer the first question at most. Only a reviewer comparing the claim against the cited passage answers the second. Keep these as separate checkboxes in your process, and never treat a provenance signal as proof that the content is correct.
What the official guidance says about disclosure
Disclosure of AI use is a reader-facing choice, and it is also a legal question in some places.
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- Google suggests sharing how content was created in a way that makes sense for the audience, including context about automation where it is useful.
- The European Commission states that the transparency obligations in Article 50 of the EU AI Act apply from 2 August 2026. Its accompanying code describes provider marking and detection duties, and deployer labeling duties for specified content. The code is voluntary; the underlying Article 50 requirements are legal obligations.
- The same code says deployer disclosure for AI-generated or manipulated public-interest text does not apply where the publication has undergone human review and is subject to editorial responsibility. Whether that exception fits your own output depends on your role and your review process.
This is a summary of the Commission’s public statements, not legal advice for any particular publisher. Check the current text and decide with counsel whether the rules apply to your content and role.
Citing sources in an AI-generated video
Viewers rarely click through to a description box, so a citation needs to appear where the claim does. Practical placements include:
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- A short on-screen source line beneath any chart or statistic, naming the publisher and year.
- A numbered source list in the video description, with each number matched to a scene in the ledger.
- An end card that lists the primary documents, with the update date of each.
Keep the citation close to the claim it supports. A general credits list at the end does not show which sentence rests on which document.
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Limits of automated detection
Automated detection tools are useful for some questions and misleading for others. OpenAI’s Content Provenance API, for example, checks supported images and audio for specific OpenAI signals. OpenAI’s own API documentation says it is not a general-purpose AI detector and does not identify content generated by every AI system, so a missing signal does not prove that content was made without AI.
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OpenAI has also described its text watermark detection in its own evaluations. Those figures, reported in 2026, come from one example domain and measure detection of its own watermark, not accuracy or human contribution:
| Condition (OpenAI’s reported evaluation, 2026) | Reported detection rate |
|---|---|
| 200-token passages, at a 1% false-positive target | About 80% |
| 400-token passages, at a 1% false-positive target | About 95% |
| 400-token passages with 10% of words replaced | About 66% (down from about 92% unedited in the same evaluation) |
| 400-token passages with 25% of words replaced | About 17% |
The practical lesson is that editing weakens text signals, and shorter or constrained text is harder to check. A ledger that tracks sources does not depend on any of these signals.
Comparing traceability workflows
If you are choosing between a spreadsheet, a project tool, or a fully manual process, compare them on these criteria rather than on feature lists:
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- Claim-level linkage: can each factual statement be tied to a source and a publication location?
- Revision handling: does a changed scene trigger a review of its sources and claims?
- Evidence detail: can reviewers keep the passage, dataset, or calculation behind each claim?
- Provenance versus accuracy: does the workflow keep origin signals separate from factual verification?
- Reader context: can the team explain AI use and sourcing without implying that provenance signals prove correctness?
These criteria are inferred from the goals of traceability and the limits described in the official guidance. They are not a published rating system, and no particular tool is required to apply them.
A spreadsheet with one row per claim and the fields above meets every criterion, which is why it is a sensible starting point for most small teams.
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