The short versionCapture the reason, not just the verdict. A corrected label improves the next model; a recorded reason improves the standard.
The override that vanishes
A reviewer looks at a flagged part, decides the model is wrong, and clears it. In most systems what gets stored is the outcome: cleared. The reasoning — that this class of mark is cosmetic on this component, that this vendor's surface finish reads as a scratch under this light — stays in the reviewer's head, where the next shift cannot reach it and where it leaves the building when they do.
That is all anybody means by tribal knowledge. It is not mystical. It is feedback with nowhere to go.
An override with no recorded reason is an expert opinion the company rented for four seconds.
Four steps, and the third is the one that matters
The loop itself is unremarkable, and most vendors will draw you the same diagram. The difference is entirely in whether the third step exists.
- The model flags a likely defect and shows what it is uncertain about.
- A reviewer judges it against the quality criteria that actually apply to this part.
- The system records the decision, the reason and the production context together, as one entry.
- Later model revisions and the dashboards both read that history.
What comes out of it
Two things, and the second is usually the surprise. The obvious one is that the model improves against the cases your line really produces rather than the cases a public dataset happens to contain. The less obvious one is that a cluster of corrections all pointing the same way is often not a model problem at all — it is a quality standard that is ambiguous, and now you can see which clause of it.
Human accountability does not move anywhere in this. The reviewer still decides. The loop only stops that decision being the last anyone hears of it.
Written about InspectionAI
