Scale governance to the high-risk: Which approach best | AIGP
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Scale governance to the high-risk: Which approach best reconciles these competing factors?

AIGP Understanding the Foundations of AI Governance Hard

For a safety-critical, regulated diagnostic use, governance must scale to the risk even when team maturity is limited.

The question

A mid-size medical-device maker with a young AI team wants to deploy a diagnostic model in a highly regulated, safety-critical setting, but fears heavy governance will slow its small team. Which approach best reconciles these competing factors?

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  1. Copy the minimal governance that a low-risk consumer app would use so the small team is not overburdened.
    Almost right on proportionality, but a diagnostic device is not low-risk and needs far stronger controls.
  2. Postpone all governance work until after launch so the small team can ship the diagnostic model on schedule.
    Plausible but wrong: deferring governance in a safety-critical setting exposes patients and the firm to serious risk.
  3. Apply only lightweight startup-style governance because the AI team is small and still relatively immature.
    Almost right that maturity matters, but a safety-critical regulated use overrides team size in setting governance depth.
  4. Scale governance to the high-risk, regulated use case even though team size and maturity are still limited. ✓
    Correct: governance intensity should track risk and industry; a safety-critical, regulated use warrants strong controls despite limited maturity.
The trap
Letting small team size or delivery speed, rather than the use case's risk, dictate how much governance to apply.

How to remember it

For a safety-critical, regulated diagnostic use, governance must scale to the risk even when team maturity is limited.

How many of these would you get right?

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