This question evaluates a candidate's competency in designing human-in-the-loop moderation systems, testing system architecture, scalability, reliability, privacy/compliance, operational workflow design, ML feedback loops, reviewer UX, and SLA-driven incident handling.
You are designing a human-in-the-loop (HITL) review subsystem for a large-scale safety platform that moderates user-generated content (UGC) across text, images, and audio (including live voice). Automated detectors (ML models and rules) generate “detections” with metadata (content IDs, model type, confidence, policy category, timestamps). Some detections require immediate enforcement; others need human review for accuracy, context, or policy interpretation.
Design and explain the end-to-end HITL subsystem, covering:
State reasonable assumptions where necessary and be explicit about trade-offs.
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