The Honest Classifier: Why 'No Answer' Beats False Confidence

This article reveals a critical flaw in automated compliance systems: they can fail silently by producing confident-looking results despite insufficient evidence. The real danger isn't system crashes but 'green rows' that hide weak or missing data—exactly what happened in the tariff classifier bug that persisted incomplete results. Building trustworthy classification systems requires them to openly reject uncertain cases and preserve evidence of doubt, rather than flatten all outcomes into successful completions.