Accuracy is unestablished for affected seasonal products | AIGP
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Accuracy is unestablished for affected seasonal products: What does this evidence most directly support?

AIGP Understanding How to Govern AI Development Hard

Strong labeled accuracy cannot cover categories with systematically missing outcomes; their performance remains unestablished.

The question

A retail demand model was evaluated on a carefully sampled test set, but labels are missing for many products and concentrated in seasonal categories. The labeled subset shows strong accuracy. What does this evidence most directly support?

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  1. The strong labeled accuracy represents all products.
    Selection and missingness concentrated in seasonal categories make broad generalization unsupported without evidence for those products.
  2. The unlabeled products can be treated as correct predictions.
    Unlabeled outcomes provide no correctness evidence and cannot be converted into validation results merely because predictions exist.
  3. Accuracy is unestablished for affected seasonal products. ✓
    Concentrated missing labels limit evaluation coverage, so strong results on labeled items cannot establish performance for affected seasonal products.
  4. Adding more labeled products automatically resolves the limitation.
    Additional labels help only if they cover the affected categories appropriately and are measured using suitable evaluation procedures.
The trap
Ask whether missing outcomes are concentrated; if so, labeled accuracy may not generalize.

How to remember it

Strong labeled accuracy cannot cover categories with systematically missing outcomes; their performance remains unestablished.

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