A global e-commerce platform uses Claude for automated customer review moderation. The system removes reviews flagged by Claude as violating community guidelines (spam, hate speech, false claims). During a post-deployment audit, the data team discovers a statistically significant disparity: reviews written in non-English languages are removed at 3x the rate of English reviews, even after controlling for actual policy violations. English-language spam is passing moderation at higher rates. What safety and ethics response is appropriate?

A 3x disparate impact on non-English content in an automated removal system is a significant fairness issue with real business and ethical consequences: it may remove legitimate non-English reviews at higher rates (harming non-English-speaking sellers and buyers), and it constitutes differential moderation quality across user demographics. The correct response is to pause the affected automated action (removal) while investigating root cause — running a biased system at full speed compounds harm. Root cause analysis is essential before remediation because the fix differs depending on cause: prompt sensitivity (improve non-English examples), embedding bias (switch embedding model), or threshold miscalibration (per-language thresholds). Option A (improve English examples) addresses the wrong side of the disparity and doesn't investigate the root cause. Option C (statistical correction factor) is a crude post-hoc fix that may over-correct and doesn't address the underlying model quality issue. Option D (human review for non-English) is a reasonable temporary mitigation but operationally unsustainable at scale and treats non-English content as inherently requiring more scrutiny.