Disclosure requirements have gotten more specific, not less
As AI-generated and AI-assisted content has become a standard part of marketing production, platform policies and regulatory expectations around disclosing that involvement have grown correspondingly more specific — a single generic internal policy written once, without checking each platform's actual current requirements, risks falling short of what any particular platform or jurisdiction now expects.
Why "technically disclosed" and "actually transparent" aren't the same thing
A disclosure buried in fine print, a footer, or a rarely-read terms page may satisfy a narrow reading of a policy requirement while functioning, in practice, as close to no disclosure at all from the perspective of a typical viewer who never encounters it — the gap between technical compliance and genuine transparency is exactly where trust risk lives, independent of formal compliance status.
Why synthetic media deserves its own distinct disclosure standard
AI-generated or AI-altered images and video carry a meaningfully higher risk of being mistaken for authentic footage than AI-assisted text does, since a viewer's default assumption about an image or video is generally that it depicts something real — synthetic media disclosure is a distinct, higher-stakes category that deserves its own specific practice rather than being folded into general AI-content policy.
Why customer-facing AI disclosure is a separate concern from content disclosure
Disclosing that a piece of marketing content was AI-generated is a different concern from disclosing that a customer is currently interacting with an AI system (a chatbot) rather than a human — a customer believing they're speaking with a person when they're not is a distinct kind of disclosure gap with its own trust implications, worth checking separately.
Why consistency matters as much as any individual disclosure decision
Disclosing AI involvement in some content but not similar content elsewhere, based on individual convenience or perceived stakes, undermines both the reliability of the disclosure practice and the trust it's meant to build — consistent application, verified through periodic audit, is what makes disclosure meaningful rather than selective and ad hoc.
Frequently Asked Questions
Platform policies and regulatory expectations around AI content disclosure have grown increasingly specific and vary by platform — a generic internal policy written once without checking each platform's actual current requirements risks falling short of what a specific platform or jurisdiction now expects.
A disclosure a typical viewer never actually encounters or reads functions, in practice, similarly to no disclosure from the audience's perspective — the gap between narrow technical compliance and genuine transparency is where real trust risk lives, independent of formal compliance status.
A viewer's default assumption about an image or video is generally that it depicts something real, giving synthetic media a meaningfully higher risk of being mistaken for authentic footage than AI-assisted text carries — this higher stakes profile warrants its own specific disclosure practice.
They're distinct concerns — one is about whether marketing content was AI-generated, the other is about whether a customer currently believes they're talking to a human when they're actually interacting with an AI system (like a chatbot), each with its own trust implications worth checking separately.
No. All scoring runs and saves in your own browser via localStorage — nothing is uploaded, making this safe to use for an internal marketing compliance review.