Link input provenance: Which retention approach best supports | AIGP
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Link input provenance: Which retention approach best supports traceability?

AIGP Understanding the Foundations of AI Governance Hard

Traceability requires linked origins, transformations, versions, and outputs; retaining materials without relationships leaves the causal record incomplete.

The question

A retailer must reconstruct why a demand forecast changed after a supplier updated its model. The team has output snapshots, but not linked input origins, transformations, or model versions. Which retention approach best supports traceability?

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  1. Retain only final forecasts and the supplier’s latest benchmark report.
    Final outputs and benchmarks show results and general performance, but cannot reconstruct changed inputs, transformations, or versions.
  2. Archive every available source file without linking files to forecast decisions.
    Broad archival preserves materials, but unlinked files do not establish which data and versions produced each decision.
  3. Rebuild forecasts periodically using current data and document the resulting accuracy.
    Periodic rebuilding evaluates current performance, but cannot explain the historical forecast change or its supplier dependency.
  4. Link input provenance, transformations, model versions, and resulting forecast records. ✓
    Linked provenance and version records connect changing inputs and processing steps to the specific forecast produced.
The trap
When historical reconstruction matters, choose linked provenance and version records, not merely more archived artifacts.

How to remember it

Traceability requires linked origins, transformations, versions, and outputs; retaining materials without relationships leaves the causal record incomplete.

How many of these would you get right?

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