Show the factors behind that product’s forecast change | AIGP
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Show the factors behind that product’s forecast change: Which improvement most directly addresses

AIGP Understanding the Foundations of AI Governance Medium

Showing factors behind a specific forecast addresses how that output was produced.

The question

A retailer tells planners that an AI forecast was produced from recent sales and weather data, but planners cannot determine why a particular product’s forecast changed. Which improvement most directly addresses explainability rather than transparency?

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  1. Show the factors behind that product’s forecast change.
    Providing relevant factors for the specific output addresses how that forecast was produced, which is the explainability need described.
  2. Publish a system-use notice.
    A general notice improves transparency about the system, but it does not explain the particular product forecast.
  3. Display the product’s forecast history in a planning dashboard.
    A history of output values improves visibility into what changed, but does not necessarily explain why the system changed it.
  4. Document the data sources and treat that documentation as proof of forecast accuracy.
    Data-source documentation can improve transparency, but it neither explains the specific output nor proves accuracy or causal validity.
The trap
Ask whether the request concerns what happened or how a particular output was produced.

How to remember it

Showing factors behind a specific forecast addresses how that output was produced.

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