Why Your AI Accuracy Metric May Be Measuring the Wrong Thing

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A developer building an AI translation system relied on a single pass/fail accuracy score combining three axes: correct meaning, correct language, and structural integrity. The system's scores kept improving, suggesting the model was performing well, but reader feedback revealed a blind spot: translations felt flat and lifeless despite being technically correct. The developer realized the metric never captured the quality that actually mattered to readers — emotional resonance and readability. This reflects Goodhart's Law, where optimizing for a proxy measure causes the true goal to be neglected. The episode highlights how defining 'accuracy' is a deliberate choice, and a poorly chosen definition can mask real shortcomings even as scores rise.

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