Reassess the affected population and data assumptions: Before | AIGP
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Reassess the affected population and data assumptions: Before relying on the earlier validation, what should

AIGP Understanding How to Govern AI Development Medium

A changed applicant population requires reassessing context, evidence, and affected people before relying on prior validation.

The question

A company validated an employment-screening model using internal applicants. It now proposes screening external applicants, including people with nontraditional work histories. Stakeholders report differences in documentation access and disability-related effects. Before relying on the earlier validation, what should be reassessed first?

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  1. Increase model complexity to accommodate broader applicant histories.
    Changing model complexity does not establish whether the earlier validation remains representative or whether new harms may arise.
  2. Reassess the affected population and data assumptions. ✓
    The proposed use changes the applicant population and evidence context, requiring reassessment of representativeness, accessibility, and potential impacts.
  3. Move all screening decisions to human reviewers before reviewing the changed context.
    Human review may be a control, but reviewers still need context-specific information about applicants, limitations, and impacts.
  4. Compare document formats used by external applicants.
    Document formats may matter, but this narrow check does not reassess the changed population, accessibility conditions, and evidence limits.
The trap
Revisit population and evidence assumptions before changing model design or oversight.

How to remember it

A changed applicant population requires reassessing context, evidence, and affected people before relying on prior validation.

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