Create a Review Queue for User-Generated Content
Sort a backlog of reports and submissions for human review without letting a model make the final moderation call.
Make policy labels operational
Labels such as "unsafe" are too vague for a review queue. Define the policy area, severity, confidence, and the action a reviewer should take. Include a category for uncertain cases. It is safer than forcing every submission into a confident label.
Write escalation rules before you process a backlog. Threats, self-harm, child safety, legal requests, and account compromise need a person immediately, regardless of the model output.
Keep the original submission beside the suggestion
A reviewer needs the submitted text or media reference, the report reason, the proposed label, the evidence phrase, and the model confidence. Do not show a bare label and ask people to trust it.
Create a small labeled sample with policy specialists. Measure false negatives by policy area. The error that hurts most is often the one a broad accuracy number hides.
Return policy_area, severity, evidence_quote, confidence, and reviewer_action.
Allowed reviewer_action values: review_now, standard_review, needs_specialist.
Always use needs_specialist for threats, self-harm, suspected child exploitation, account takeover, or legal removal requests. Do not recommend a final enforcement action.Process an old queue asynchronously
A historical review backlog is a batch problem. DigitalOcean Batch Inference accepts JSONL requests and returns results after asynchronous processing. Keep the job scoped to text requests and make sure each source record has a unique ID for the result join.
Run a limited batch and sample every label before routing a full backlog. Stop the run if reviewers see a systematic error. A queue that moves faster in the wrong direction is worse than a slow queue.
Verify this workflow before scaling it
- Input
- Use a labeled sample of reports with the original submission, report reason, policy area, and final reviewer outcome.
- Build
- Create a batch contract that proposes severity and reviewer action but never a final enforcement decision.
- Expected result
- Reviewers see the original evidence beside the suggestion, with urgent safety cases routed to a specialist.
- Stop if
- Stop if a policy label lacks an evidence quote, an urgent report is ordinary review, or the model recommends enforcement.
- Next step
- Compare suggestions with reviewer outcomes and revise the policy instruction before processing a larger queue.
Measure reviewer outcomes, not model confidence
Track time to review, agreement with the final reviewer outcome, appeals or reversals, and the number of urgent items caught by escalation rules. Confidence is only a sorting hint.
Keep a dated copy of the policy and instruction used for each batch. That gives your team a way to explain a change in outcomes and roll back a bad revision.