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DATA-LABELING-FOR-AI5 MIN READ

Sort Risks Before Launch

Classify labeling launch risks using FMEA-style severity and detectability thinking.

Place each labeling launch risk in the action bucket it deserves. Prevent before launch Detect with QA control Monitor after launch New schema reorders label IDs while export code still expects old order One label definition has a minor typo that does not change meaning Annotators may miss the rare legal-hold edge case Duplicate source rows can enter train and evaluation splits Low-confidence rationale field may be skipped by some reviewers A small region-specific slang case appears once in the pilot Gold examples were built from the same rows as training candidates Night-shift annotators are new to one exclusion rule

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