Substantiate claims and assess foreseeable accuracy risks | AIGP
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Substantiate claims and assess foreseeable accuracy risks: What missing control most directly addresses both

AIGP Understanding How Laws, Standards and Frameworks Apply to AI Hard

The missing control is evidence-based substantiation paired with foreseeable-risk assessment, not labeling, disclaimers, or supplier assurances.

The question

For a US consumer-facing travel-support tool, assume product review, supplier oversight, incident procedures, and ordinary legal prerequisites are satisfied. Testing shows accuracy varies by itinerary type, while marketing claims broad reliability. What missing control most directly addresses both concerns?

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  1. Add a general user disclaimer about possible errors.
    A disclaimer may inform users, but it does not substantiate capability claims or evaluate foreseeable accuracy risks across itinerary types.
  2. Ask the supplier to confirm the model performs reliably.
    Supplier confirmation is useful evidence, but unsupported assurances do not independently substantiate public claims or replace the buyer’s risk evaluation.
  3. Describe the system as AI-assisted rather than automated.
    Changing terminology does not establish accuracy or reduce foreseeable risks; consumer-protection responsibilities can apply regardless of the label used.
  4. Substantiate claims and assess foreseeable accuracy risks.
    Evidence supporting bounded claims and documented foreseeable-risk assessment directly address unsupported reliability statements and observed itinerary-specific performance variation.
The trap
Separate evidence supporting a claim from communications that merely qualify or rename the technology.

How to remember it

The missing control is evidence-based substantiation paired with foreseeable-risk assessment, not labeling, disclaimers, or supplier assurances.

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