AI Content Moderation for a Community Platform
A moderation layer that scores every post against the platform's own guidelines, removes clear violations, queues borderline content with a reason and keeps humans on appeals.
Moderators reviewed content in report order, so serious violations could sit for hours behind trivial reports. Burnout was high and decisions were inconsistent.
The approach.
- 01Turned the community guidelines into a structured policy the model applies.
- 02Scored text and images on upload and ranked the review queue by severity.
- 03Auto-removed only high-confidence, high-severity categories, with the rest sent for review.
- 04Measured agreement with senior moderators weekly on a blind sample.
Claude classifies each item against the written policy, explains which rule applies and assigns a severity that orders the human queue.
How we measure it.
Time to action comes from the moderation log. Review volume is counted per day. Agreement is measured weekly on a blind sample graded by senior moderators.
Before you ask.
People. Beyond Technologies keeps every appeal with a human moderator and feeds the outcome back into the evaluation set.
Yes. The policy the model applies is the platform's own, written as rules your team can edit.
Want this measured on your own data? Start with a pilot.
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