AI confidence scores: useful signals, not guarantees

Ask how the score is produced

A score may come from a document-processing service, an explicit statistical model or a language model’s own assessment. Those are not interchangeable. Ask what the number represents, what evidence supports it and whether it has been evaluated for this task.

Do not read “90% confidence” as “90% of these answers will be correct” unless there is evidence for that interpretation. Fluency, a plausible explanation and a numerical score are all things a model can generate; none independently proves that the source supports the value.

Define correctness at the field level

For a date, correctness may require the exact day, month and year with the right interpretation of the source format. For a name, a spelling variation may need manual reconciliation. For an identifier, a single wrong character can refer to another record.

Agree those definitions before reviewing the results. Then look at the combination of score, actual correctness and consequence. An overall accuracy figure can conceal the errors that matter most to the process.

A small worked example

Illustrative example. A pilot contains 100 fields labelled high confidence, of which 92 match the agreed reference. The observed correctness for that group is 92 out of 100. It does not establish that every future field bearing that label has the same probability of being correct, especially if the next batch has different document layouts.

Inspect the eight errors. Were they harmless formatting differences, wrong amounts or wrong identities? A review policy that treats all eight as equivalent may be commercially convenient but operationally unsuitable.

Use the score to organise attention

NIST’s evaluation work highlights uncertainty in performance measurement and the need to make evaluation assumptions explicit. A small sample is evidence to learn from, not a permanent guarantee. [1]

Where this leaves your REDA workflow

If your configured document workflow includes confidence indicators, treat them as review signals until their usefulness is demonstrated. REDA AI’s configuration can shape the procedure and output; that does not itself establish statistical calibration.

Start with a reviewer who can compare proposed values with the evidence. Decide which fields must always be checked, how unresolved readings are handled and what would trigger another evaluation. The aim is a useful review process, not an impressive-looking score.