Document data provenance and representativeness gaps | AIGP
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Document data provenance and representativeness gaps: Which evidence is most important first?

AIGP Understanding How Laws, Standards and Frameworks Apply to AI Easy

Provenance and representativeness evidence directly addresses the known regional and seasonal data-quality concerns.

The question

A retailer uses an EU AI Act high-risk demand-planning system, assuming the applicable duties apply. Training data combine stores with different seasonal patterns, and missing regional values are known. Which evidence is most important first?

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  1. Compare forecast accuracy with the supplier’s published benchmark.
    Benchmark comparison informs performance but does not establish provenance, representativeness, or handling of known regional gaps.
  2. Limit records to the final cleaned dataset used in production.
    A final dataset alone omits lineage and decisions about seasonal variation, missing values, and suitability for intended use.
  3. Document data provenance and representativeness gaps. ✓
    Data-governance evidence should show where data came from and whether coverage and quality fit the system’s intended purpose.
  4. Obtain store managers’ general approval of the training dataset.
    Manager approval may support governance, but it does not document data origin, coverage, quality, or missing-value treatment.
The trap
Match evidence to the stated data defect; known gaps call for lineage, coverage, and treatment records.

How to remember it

Provenance and representativeness evidence directly addresses the known regional and seasonal data-quality concerns.

How many of these would you get right?

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