Evaluate 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
This is a review-queue evaluation playbook. The queue record below is an output design, not a deployed moderation system. Complete the linked batch API integration before processing a historical text sample. Keep urgent reports in your existing immediate escalation process.
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.
- Use the three fictional historical cases below. Keep the expected routes out of the model input. These are offline examples, not a channel for urgent reports.
- Follow the linked batch guide to build provider-valid JSONL requests containing the queue instruction and one case each. Upload the file, create the job, retain its ID, poll completion, and download results and errors.
- Join outputs by the original ID into a local review worksheet. Show the original text, proposed route, and a blank final human outcome. Stop if an ID is missing or the urgent case is not routed to a specialist.
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.
Expected human checks: UGC-01 → standard_review for possible spam; UGC-02 → standard_review with missing context flagged and no enforcement recommendation; UGC-03 → needs_specialist for account takeover. All final outcomes remain unset until a reviewer acts.
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.{
"submission_id": "report-801",
"report_reason": "threat",
"policy_area": "credible_threat",
"severity": "high",
"evidence_quote": "[minimal relevant excerpt]",
"confidence": "medium",
"reviewer_action": "needs_specialist",
"final_reviewer_outcome": null
}[
{
"id": "UGC-01",
"text": "The same advertisement was posted five times."
},
{
"id": "UGC-02",
"text": "The report says this joke is harassment, but the surrounding conversation is missing."
},
{
"id": "UGC-03",
"text": "The report says an unknown person took control of the account."
}
]Make each queue state explicit
A queue should tell reviewers what happened next, not merely what a model guessed. Keep the suggested action separate from the final reviewer outcome so quality checks can find unsafe routing and reversals.
For example, new items enter needs_specialist or standard_review; a reviewer then marks resolved, escalated, or returned_for_policy_clarification. Only your authorized reviewer makes an enforcement decision.
- needs_specialist: immediate specialist attention; do not wait for confidence to rise.
- standard_review: normal reviewer workflow with the original submission visible.
- returned_for_policy_clarification: the policy label or instructions do not support a safe route.
- resolved or escalated: final human outcome, owner, and timestamp recorded.
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.
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.
Check your result
- 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.