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A moderation classifier (GPT-4o zero-shot) hits 96% accuracy on a balanced test set but the remaining 4% is concentrated on borderline cases — which is exactly the population humans most want right. False negative rate on borderline-harmful content is ~18%.
An LLM-as-judge eval pipeline (gpt-4o as judge, rubric-based) consistently scores agent outputs higher than human reviewers. The gap is ~1.3 points on a 5-point scale. Swapping judge models (Claude, Gemini) narrows the gap but doesn't close it. The issue blocks us from trusting the eval for regression detection.