AI-generated marketing content disclosed when misleading re: authorship
Why it matters
The FTC's 2024 AI guidance distinguishes between AI as a writing tool and AI-generated content that would be material to a consumer's decision — such as a founder letter, an expert opinion, or personalized outreach implying individual human judgment. A blog post attributed to a named human author but generated by an LLM, or a CEO voice email campaign written by AI, deceives consumers about the source of the advice they are relying on. Where authorship is a credibility signal, the FTC Act §5 deception standard applies to AI authorship just as it does to fabricated human authorship.
Severity rationale
Low because AI authorship deception requires consumers to make decisions based on implied human expertise they would not have followed if they knew the source — the harm is real but depends on the weight the consumer placed on the implied authorship.
Remediation
Add an AI-assistance disclosure to any content where authorship affects credibility, and restructure AI-written email campaigns to avoid false first-person voice.
function BlogPostMeta({ post }: { post: Post }) {
return (
<div className="text-sm text-gray-600">
<span>By {post.author}</span>
{post.aiAssisted && (
<span className="ml-3 bg-gray-100 px-2 py-0.5 rounded text-xs">
Written with AI assistance
</span>
)}
<span className="ml-3">{formatDate(post.publishedAt)}</span>
</div>
)
}
// AI-generated mass email — avoid false individual voice
// AVOID: "I personally wanted to reach out to you, [name]..."
// (when the email is AI-generated to thousands of users)
//
// PREFER: "The [Product] team wanted to share..." or a clearly
// branded automated format that does not imply individual
// human authorship
The test: would a reasonable consumer make a different decision if they knew the content was AI-generated? If yes, disclose it. Commodity copy — FAQs, error messages, standard product descriptions — does not require disclosure where authorship is not a credibility factor.
DetectionPro
The detection method for this pattern is part of AuditBuffet Pro.
The full method (what to look for, pass and fail conditions, and how results get reported) ships inside the FTC Consumer Protection Audit. The pattern definition, severity rationale, and remediation remain openly licensed under CC-BY-4.0.
External references
- external · ftc-ai-guidance-2024 — FTC Guidance on AI and Deceptive Practices (2024)
- external · ftc-act-section-5 — FTC Act Section 5 — Unfair or Deceptive Acts or Practices
Taxons
History
- 2026-04-18·v1.0.0·Initial import from ftc-consumer-protection·automated