Turn scores into review priorities
A review queue should translate detector output into clear priorities: high-risk documents, low-confidence cases, policy-sensitive submissions, and items waiting for reviewer notes.
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Design AI detection review queues for risk bands, reviewer assignment, evidence triage, escalation rules, and responsible human decisions.
Open core guideA review queue should translate detector output into clear priorities: high-risk documents, low-confidence cases, policy-sensitive submissions, and items waiting for reviewer notes.
Education, publishing, and enterprise teams often need different reviewer roles. Queue metadata should include document type, source, risk band, confidence, owner, due date, and policy status.
The queue should capture reviewer notes, supporting evidence, requested revisions, author context, and final disposition so teams can explain decisions without relying on a score alone.
A useful queue includes document identifiers, risk bands, confidence, highlighted evidence, reviewer assignment, policy status, notes, timestamps, and final resolution fields.
No. High-risk results should be prioritized for review. Sensitive decisions should include human judgment, evidence, context, and a documented follow-up path.