Show the factors behind that product’s forecast change: Which improvement most directly addresses
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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- 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.
- Publish a system-use notice.A general notice improves transparency about the system, but it does not explain the particular product forecast.
- 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.
- 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.
How many of these would you get right?
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