Confident extraction errors may persist on handwritten | AIGP
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Confident extraction errors may persist on handwritten: Which residual risk remains?

AIGP Understanding How to Govern AI Development Medium

The cheaper model’s threshold does not catch confident errors on handwritten amendments, leaving a documented extraction risk.

The question

A financial-document processor can use a more capable model for difficult forms or a cheaper model with a confidence threshold. The cheaper option reduces cost, but testing shows confident extraction errors on handwritten amendments. Which residual risk remains?

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  1. Additional training data could improve performance across every document type.
    Additional data may help selectively, but it cannot be assumed to resolve all document types or the known confident-error pattern.
  2. The more capable model may require additional vendor support.
    Support needs are plausible, but they do not identify the documented residual risk associated with cheaper-model extraction errors.
  3. Confident extraction errors may persist on handwritten amendments.
    The threshold does not catch errors made confidently, leaving a specific reliability risk for handwritten amendments despite lower resource use.
  4. Processing costs may exceed the original budget.
    The cheaper model is specifically proposed to reduce cost; the evidence instead identifies accuracy failures on a difficult document class.
The trap
A confidence threshold is not a substitute for testing known difficult inputs, especially when errors can be confidently produced.

How to remember it

The cheaper model’s threshold does not catch confident errors on handwritten amendments, leaving a documented extraction risk.

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