PRODUCTION SCENARIO
A bank validated its customer assistant against an English-only test set and an English-only fairness review. It launches in nine more languages and several regional English dialects next month.
What should the responsible AI lead require before launch?
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Answer: A quality and fairness test set built in each launch language and dialect
Google states that the majority of its benchmarks, including all of its fairness evaluations, are in English, and that quality can be worse for non-English languages and for English varieties with less representation in training data. The English result therefore proves nothing about the other ten varieties, so the evidence has to be rebuilt per language and per dialect before launch. Red-teaming three sample languages is a real safety exercise that probes for harmful outputs and leaves six launch languages unmeasured. A review queue for the first month is monitoring, not evidence: it detects the gap after customers have met it. A translation-quality benchmark measures how well text converts between languages, not how well the assistant answers in each one.