The data is fit for automated rejection: Which prior assumption | AIGP
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The data is fit for automated rejection: Which prior assumption no longer holds?

AIGP Understanding the Foundations of AI Governance Medium

Data suitable for a dashboard is not automatically representative or complete enough for automated batch rejection.

The question

A manufacturer approved sensor data for an internal quality dashboard. It now proposes using the same data to automatically reject production batches, although labels came from one plant and sensor coverage is incomplete. Which prior assumption no longer holds?

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  1. The supplier owns all deployment decisions.
    The organization remains responsible for governing its changed use; supplier involvement does not transfer deployment decision authority.
  2. The sensors provide operational information.
    Incomplete coverage does not mean sensors provide no operational information; the issue is fitness for automated rejection.
  3. The data is fit for automated rejection.
    Single-plant labels and incomplete sensor coverage undermine the assumption that data is representative and complete for batch rejection.
  4. The data supports the original dashboard.
    The data may still support descriptive dashboarding, so this assumption does not necessarily fail under the changed proposal.
The trap
Reassess data when moving from observation to consequential action, especially with incomplete coverage or localized labels.

How to remember it

Data suitable for a dashboard is not automatically representative or complete enough for automated batch rejection.

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