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Field note

Designing for the human reviewer

Agreement scores are useful, but evidence, controls, and correction paths make an editorial agent operational.

EvaluationHuman reviewEditorial AI

An 84% agreement score sounds like a finished system. It is not. It is the point at which the system becomes usable by a reviewer — and the reviewer's experience decides whether it survives contact with a real morning.

Three things a reviewer actually needs

  1. Evidence, not confidence. Show the source lines that drove the classification. A reviewer can check evidence in seconds; a confidence score tells them nothing they can act on.
  2. A fast correction path. Overriding a decision must be cheaper than living with it. If correction is slow, reviewers stop correcting and start distrusting.
  3. Visible controls. Which rules ran, which model ran, what was skipped. When the system is boring and inspectable, people trust it more, not less.

The takeaway

Optimise the reviewer's minute, not the model's score. The score follows.

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