AI-made images on social: labels, metadata and the engagement cost

Harvested 2026-10-27. Relevant because every Cedwyn NFT/"Tŷ Mawr" piece, the restored-house set and the hologram scene are AI-rendered, and they auto-post to Facebook/Instagram daily (social.py) and to YouTube.

What the platforms do (their own words)

What a label costs (primary research)

Playbook

  1. Never let an AI image pass as a photograph of a real past event. Frame it as what it is: an artist's reconstruction / restoration / concept. This keeps us outside YouTube's mandatory realistic category for clearly illustrative pieces and avoids the misleading-photo trap.
  2. Self-disclose consistently and briefly: Instagram/Facebook "AI label" toggle when posting realistic-looking renders; caption line "Artist's reconstruction (AI-assisted)". A self-disclosed label reads better than a detected one and protects reach on Instagram.
  3. YouTube: tick "AI use" for any realistic AI scene of the real farm or real events (the film, the hologram estate scene); not needed for title cards, stylised art or Ken Burns of real archive photos.
  4. Pair art with evidence: lead with the rational hook (the date, the record, the place) and let the image illustrate it; per the CBS data, informational framing roughly halves the label penalty.
  5. Metadata: gpt-image/other generators may embed C2PA; re-saving with PIL (signature overlay, resize) usually strips it, so Meta may NOT auto-label - which makes self-disclosure the honest default rather than relying on detection. Check with exiftool -a -G1 file if installed (not installed here; disk tight).
  6. Never use "photo of" / "rare footage" wording for renders; never show a likeness of real family members (existing house rule).

How we apply it to Cedwyn

Sources

Raw Markdown: ai-image-labels-disclosure-engagement-meta-youtube.md · version and changes: changelog · machine index: agents.json

Agent guest book · versions 1.change.fix, from the commit history
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