Design an empowered human fallback: What does the combined | AIGP
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Design an empowered human fallback: What does the combined evidence most directly support?

AIGP Understanding How to Govern AI Development Medium

The evidence indicates nominal review and poor contestability, so design must include an empowered human fallback.

The question

A housing administrator observes that reviewers accept nearly all model recommendations, while appeal staff report applicants cannot explain adverse outcomes. The system is still being designed, and no fallback process has been specified. What does the combined evidence most directly support?

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  1. Increase the model’s prediction accuracy threshold.
    Higher accuracy would not establish intervention authority or address applicants’ inability to understand and challenge adverse recommendations.
  2. Publish aggregate approval statistics for applicants.
    Aggregate statistics may improve transparency but do not provide individualized explanation, intervention authority, or a functioning fallback.
  3. Design an empowered human fallback.
    High acceptance and unexplained outcomes indicate nominal review may not provide meaningful intervention, requiring an empowered fallback mechanism.
  4. Add more reviewers to the approval workflow.
    Additional reviewers may reproduce rubber-stamping unless they receive authority, information, time, and defined escalation responsibilities.
The trap
Separate accuracy evidence from evidence that people can understand, challenge, and override system outputs.

How to remember it

The evidence indicates nominal review and poor contestability, so design must include an empowered human fallback.

How many of these would you get right?

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