AI Content Disclosure

Decide, asset by asset, which disclosures AI-generated or AI-assisted marketing content needs — by jurisdiction and by platform — and block publication when a high-risk item is unresolved. Covers EU AI Act Article 50 (applies from 2 August 2026), the FTC Consumer Reviews and Testimonials Rule (16 CFR Part 465), the FTC Endorsement Guides (16 CFR Part 255), EU and UK fake-review law, New York's synthetic performer ad law, India's synthetic-content labelling rules, and the AI-label policies of YouTube, TikTok, Meta and Google Ads.

Not legal advice. This skill turns public rules into a repeatable pre-publication check. It does not replace counsel, and every rule links its official source so you can verify it. Rules were checked against primary sources in September 2026.

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When to use this skill

Situation	Use
Launching a campaign with AI-generated video, images, voice or avatars	scripts/disclosure_checker.py on the content manifest
Publishing AI-drafted articles, reports or press notes to EU audiences	Decision tree Q4 + checker (public_interest, human_review)
Deploying a website chatbot or AI voice agent in the EU	Checker (type: chatbot) + wording library
Importing, soliciting or displaying reviews and testimonials	scripts/review_authenticity_linter.py
Briefing influencers or virtual influencers	Label templates §6 + Endorsement Guides summary
Checking a platform's own AI-label rule	references/platform-ai-label-policies.md
Political or electoral advertising	Out of scope beyond platform checkboxes — route to counsel

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Clarify First

Before running the check, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Audience regions — EU, US, New York, UK, India (each switches on different rules; "global" means all of them)
• AI involvement per asset — none / assistive / partial / generated (assistive editing triggers no AI label; generated realistic media usually does)
• Is anything a review, testimonial or endorsement? — and who wrote it, and what they received (fake and undisclosed-connection reviews are the highest-penalty items)
• Paid or organic, and which platform — platform rules differ for ads, and TikTok rejects undisclosed AIGC ads

Stop rule: ask only the 2-3 that most change the output. If the user says "just check it," proceed with the manifest as given and list your assumptions at the top of the report.

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Quick start

# 1. Fill the manifest (one entry per asset)
cp assets/content_manifest_template.json my_campaign.json

# 2. Check disclosures — exits 2 if any high-risk item is unresolved
python3 scripts/disclosure_checker.py my_campaign.json
python3 scripts/disclosure_checker.py my_campaign.json --format json --fail-on medium

# 3. Lint reviews/testimonials before display
python3 scripts/review_authenticity_linter.py reviews.json

# 4. Apply wording from references/disclosure-wording-library.md and record it
#    in assets/disclosure-label-templates.md (§1 asset disclosure record)

The shipped samples fail on purpose: sample_content_manifest.json exits 2 with 7 unresolved high-risk findings (a deepfake without an on-asset label, an unreviewed AI press note, AI-written testimonials, an undisclosed employee review); sample_reviews.json exits 2 with 6 high-risk findings.

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Core workflow: pre-publication disclosure gate

1. Inventory every asset in the release into the manifest. Include reviews shown in ads and on landing pages, chatbots and voice agents — not just "creative". [PROVEN]
2. Classify AI involvement honestly. "Assistive" means standard editing (spelling, colour, crop, background clean-up). If AI changed what the viewer sees or reads, it is partial or generated. [RECOMMENDED]
3. Run the checker. Read findings top-down by risk. FIX lines are unresolved; OK lines are satisfied by the disclosures you declared.
4. Remove, don't label, what cannot be labelled. AI-written testimonials presented as customer reviews and sentiment-conditioned incentives are prohibited outright — the checker marks them unfixable. [PROVEN]
5. Apply disclosures in three layers: legal label on the asset, platform toggle, endorsement disclosure. They stack; none substitutes for another. [RECOMMENDED]
6. Record evidence in the asset disclosure record (label text, placement, screenshots, reviewer for the editorial-control exception).
7. Re-run until exit 0, then wire the checker into the release pipeline so new assets cannot skip it.

Workflow: reviews and testimonials

1. Export the review set you plan to display or quote (own site, marketplace, ad copy).
2. Mark source, incentive, incentive_conditioned_on_sentiment, generated_by_ai, suppressed for each.
3. Run review_authenticity_linter.py. Remove every fake-or-ai-review, sentiment-conditioned-incentive, review-hijacking item; add disclosures for insider and incentivised items.
4. Publish a review-page statement (wording library) saying how reviews are verified — required in the EU (UCPD Art. 7(6)).
5. Treat near-duplicate and posting-burst as investigation leads, not verdicts.

Workflow: EU text on matters of public interest

1. Decide whether the piece informs the public on a matter of public interest (news-like, policy, health, safety, finance, elections). Product copy normally does not.
2. If yes, choose: label it, or put it under documented editorial control (named reviewer with authority to approve, alter or reject, and an entity holding editorial responsibility).
3. Editorial control is the better default for brands that publish thought leadership — it improves quality and removes the labelling duty. Keep the evidence. [RECOMMENDED]

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Rule map (what the checker encodes)

Rule ID	Trigger	Default risk	Fix
EU-AIA-50(1)	Chatbot, EU	high	AI-interaction notice at first interaction
EU-AIA-50(4)-deepfake	Realistic AI image/audio/video, EU	high	On-asset label
EU-AIA-50(4)-deepfake-artistic	Same, evidently artistic/satirical	medium	Non-disruptive disclosure
EU-AIA-50(4)-text	AI public-interest text, EU, no editorial control	high	Label or editorial control
EU-AIA-50(2)-marking	You provide the generator	medium	Machine-readable marking (legacy systems: 2 Dec 2026)
FTC-465.2-fake-review	AI-generated review/testimonial	high, unfixable	Remove
FTC-255-material-connection / FTC-465.5-insider	Connection or insider	high	Disclose in the endorsement
FTC-255-virtual-influencer	Virtual influencer	medium	"#ad" + virtual persona disclosure
NY-synthetic-performer	Paid image/video ad with synthetic performer, NY	high	Conspicuous disclosure
EU-UCPD-* / UK-DMCC-*	Fake or undisclosed-connection reviews	high	Remove / disclose
IN-IT-Rules-SGI	Realistic AI media, India	medium	Visible label
YT / TT / META / GADS	Platform AI-label rules	medium-high	Platform toggle and/or label

Full summaries with official links: references/regulation-summaries.md.

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Key dates and numbers (verify before use)

Item	Value	Source
EU AI Act Art. 50 applies	2 Aug 2026	eur-lex.europa.eu; digital-strategy.ec.europa.eu
Art. 50(2) marking, systems placed on market before 2 Aug 2026	2 Dec 2026	Digital Omnibus on AI; Commission Art. 50 FAQ
EU AI Act fine for Art. 50 breaches	up to EUR 15m or 3% of worldwide turnover	Art. 99(4)
FTC Reviews Rule effective	21 Oct 2024	ftc.gov
FTC civil penalty per violation	USD 53,088 (2025 adjustment; see 16 CFR 1.98 for current)	ftc.gov; ecfr.gov
UK DMCC fake-review ban	from 6 Apr 2025; fines up to 10% of global turnover	CMA guidance
New York synthetic performer ad law	in effect June 2026	governor.ny.gov
India IT Rules SGI amendment	in force 20 Feb 2026	meity.gov.in
Google Ads AI label setting	rolled out July 2026	support.google.com/adspolicy
Meta automated AI detection on ads	from 1 Jun 2026	about.fb.com

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Exit code contract [PROVEN]

Both scripts share the same contract so they can run as CI gates:

Code	Meaning	Who fixes it
0	No unresolved findings at or above --fail-on (default high)	Nobody
1	Tool error — bad path, malformed JSON, missing id/type/text	Whoever maintains the manifest
2	Gate failed — unresolved findings at or above --fail-on	Asset owner / campaign lead

Keep 1 and 2 distinct. A malformed manifest is a pipeline problem; a missing label is a content problem.

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Anti-Patterns

The Platform Toggle As Legal Compliance

Mistake: Ticking YouTube's or Google's AI toggle and treating the campaign as EU-compliant. Why it happens: The toggle feels official, and the platform shows a label somewhere. Instead: Platform labels can sit in a description or "About this ad" panel. For EU deepfakes, put the label on the asset at first exposure too. Google states its AI label setting does not guarantee regulatory compliance.

The "Composite" Testimonial

Mistake: Asking AI to write testimonials "based on" real survey themes and displaying them with stock names and photos. Why it happens: It feels truthful because the sentiments came from real customers. Instead: A testimonial attributed to a person who did not write it misrepresents the reviewer. Quote real customers verbatim with permission, or present aggregated survey results as statistics.

The Blanket "May Contain AI" Footer

Mistake: One site-wide footer line instead of per-asset labels. Why it happens: It is cheap and feels like cover. Instead: Art. 50(5) requires clear, distinguishable information at first exposure to the specific content. Label the asset; keep the footer as a supplementary policy statement if you like.

The Invisible Editorial Review

Mistake: Relying on the human-review exception for AI-written articles without any record of who reviewed what. Why it happens: Everyone "looked at it" in a shared doc. Instead: Record reviewer, date, substantive changes and the editorial-responsibility holder. Without evidence, the exception is a claim, not a defence.

Incentive Automations That Filter By Stars

Mistake: Sending discount codes only to customers who left 4-5 star ratings, or asking only happy NPS responders to review. Why it happens: Growth tooling makes sentiment-gated flows a checkbox. Instead: Sentiment-conditioned incentives are prohibited by FTC 465.4. Invite every customer (or a random sample) and disclose the incentive.

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Troubleshooting

Symptom	Cause	Fix
Asset shows "no disclosure rule triggered" but used AI	ai_involvement set to assistive or regions empty	Re-classify; add every audience region
Checker exits 1	Missing id/type or invalid JSON	Validate against assets/content_manifest_template.json
Finding stays FIX after adding a label	Disclosure key not in needs_one_of	Use one of the listed keys in disclosures_present
Linter misses an obvious disclosure	Disclosure phrased unusually	Put the exact text in disclosure_text; extend DISCLOSURE_RE
Too many near-duplicate hits on short reviews	Short texts share phrases	Raise --dup-threshold to 0.7-0.8

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Scripts

Script	Purpose
scripts/disclosure_checker.py	Content manifest → required disclosures per jurisdiction/platform, risk, gate
scripts/review_authenticity_linter.py	Review set → fake/AI, insider, incentive, suppression, hijacking, duplicate and burst findings, gate

Both: Python 3.8+ standard library only, --format text|json, --fail-on high|medium|low, deterministic.

References

• regulation-summaries.md — EU AI Act Art. 50, FTC 465 and 255, EU UCPD, UK DMCC, New York, India, with official links and penalties
• platform-ai-label-policies.md — YouTube, TikTok, Meta, Google Ads rules as of September 2026
• disclosure-wording-library.md — label text, placement rules, translations, endorsement wording
• disclosure-decision-tree.md — the asset-by-asset decision path and risk levels

Assets

• assets/content_manifest_template.json — manifest schema with field guide
• assets/sample_content_manifest.json — failing sample campaign (exit 2)
• assets/sample_reviews.json — failing sample review set (exit 2)
• assets/disclosure-label-templates.md — evidence record, visual tag spec, bylines, chatbot opener, influencer clause