Human-in-the-loop and the lost calibration
We designed the human review protocol for the cases AI can't handle. What we didn't account for is that those would be exactly the cases the team had lost the ability to handle.
Every human-in-the-loop protocol carries a silent assumption: that the human receiving the exception is still calibrated to judge it.
It makes sense on paper. The problem is what happens over time.
When AI takes over the main workflow, routine decisions leave the team's hands. What's left for human review are the most complex cases, the borderline ones, the ones that didn't fit any rule. Exactly the ones that demand the most judgment.
But judgment without practice loses its calibration. And the review moments grow further and further apart as the AI scales and covers more cases.
When the real exception arrives, the human responsible for the analysis has gone months without solving that type of problem independently. The protocol assumes a capability that has been silently eroded by the very system meant to support it.
I don't call this negligence or a management failure. It's the side effect of a design that treats the human as a fixed control point, without considering that control depends on a skill that must be exercised in order to exist.
The protocol was designed for the human of 18 months ago. The one operating today is a different person, shaped under different conditions, with far less repetition on the critical cases.
This needs to enter the design before the deploy, not show up as a surprise during the first crisis.
Tell me in the comments: does your company have any mechanism to keep human judgment calibrated on the cases that AI escalates? I want to understand how different teams are dealing with this silent degradation of capability.
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