The IAB is stepping into the AI accountability conversation with a new framework aimed squarely at one of marketers’ biggest open questions: when, exactly, should AI use be disclosed in advertising?

On Thursday, the trade group rolled out its first AI Transparency and Disclosure Framework, positioning it as a practical guide for brands, agencies, publishers and platforms navigating generative AI at scale.

Rather than imposing blanket disclosure rules, the framework adopts a risk-based approach that focuses on consumer impact — disclosing AI use only when it materially affects authenticity, identity or representation in ways that could mislead people.

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“We are certainly at a critical inflection point with generative AI,” David Cohen, CEO of IAB, said in a statement. “While AI is transforming how we work from ideation to execution and measurement, we must get transparency and disclosure right, or we risk losing the trust that underpins the entire value exchange.”

At the heart of the framework is a simple question: Does AI involvement meaningfully change what a consumer thinks they’re seeing, hearing or interacting with? If the answer is yes, disclosure is expected. That includes scenarios such as AI-generated or heavily synthesized images and videos depicting real-world events, synthetic voices of real people making statements they never made, digital twins placed in situations that never occurred, and conversational agents or avatars designed to simulate human interaction in ads.

Importantly, the IAB is not calling for disclosures every time AI is involved in a campaign. Routine uses — such as AI-assisted editing, optimization or background workflows — don’t automatically trigger labeling. The idea is to avoid disclosure overload while still protecting consumers from being misled.

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To make this workable across channels and platforms, the framework introduces a two-layer model. One layer is consumer-facing, using standardized text labels or visual cues like badges, icons, watermarks or interactive info elements placed near the ad creative. The other layer is machine-readable, relying on metadata standards such as C2PA to support technical compliance and downstream transparency.

For marketers, the framework is less about checking a compliance box and more about future-proofing AI adoption. As regulators, platforms and consumers scrutinize AI use more closely, having a shared industry standard gives teams a more straightforward way to balance speed, creativity and responsibility — without guessing where the line is.

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