The Accuracy Paradox: Why 99% Test Accuracy Fails in Production

This article argues that pursuing higher model accuracy on clean test sets undermines real-world AI deployment because semantic understanding requires tolerance for linguistic variation, ambiguity, and noise — not precision — and proposes evaluating semantic tolerance metrics like dialect robustness and intent diversity coverage instead of pure accuracy.

AI deploymentevaluation metricsintent recognition

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