Transparency
When should AI tell you it might be wrong?
An answer can sound certain even when it is not well supported. But warnings on every sentence may become noise.
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Labels can help people understand where media came from, but technical metadata may be lost when content is shared.
A label can disclose how content was made without determining whether its message is true. C2PA’s provenance explainer explicitly distinguishes creation history from factual accuracy. Missing provenance also does not prove a piece of media is deceptive.
Compare a disclosed AI illustration of a historical event with a genuine photograph presented as if it happened yesterday. One is synthetic and transparent; the other can mislead through context despite being a real photograph.
Consistent labels give audiences useful context.
A label does not establish whether the underlying claim is true.
Background reading for the tradeoff. Scenarios and discussion questions are editorial examples.
Explains media provenance and why a verifiable history alone does not establish factual truth.
Evaluates support for individual factual claims rather than treating a long answer as entirely right or wrong.
Sources reviewed 13 September 2026. Product documentation can change. How we use evidence
An answer can sound certain even when it is not well supported. But warnings on every sentence may become noise.
Public policies help users understand a system. Technical details can sometimes help attackers too.
A separate system might filter, rewrite, or block an answer. Should the interface make that intervention visible?