Prioritize high-consequence risks despite low likelihood | AIGP
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Prioritize high-consequence risks despite low likelihood: Which control is missing?

AIGP Understanding How to Govern AI Development Easy

Prioritize high-consequence harms even when their likelihood is low.

The question

Under the organization's internal AI risk policy, a housing administrator has recorded each risk's likelihood, consequence severity, owner, mitigation, and residual risk. The policy also requires prioritizing high-consequence harms even when unlikely. All listed controls are documented except one, and a rare error could cause homelessness. Which control is missing?

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  1. Use expected loss alone to set treatment priority.
    Expected-loss calculations can be informative, but using them alone may fail to elevate catastrophic low-probability harms required by the policy.
  2. Rank risks by likelihood alone.
    Likelihood-only ranking can underprioritize rare events whose consequences are exceptionally severe for applicants.
  3. Allocate treatment only after comparing every risk with the portfolio average.
    Portfolio averages can mask severe outliers and do not implement the policy's requirement to prioritize high-consequence harms.
  4. Prioritize high-consequence risks despite low likelihood. ✓
    The missing control is an explicit severity priority that ensures rare but potentially catastrophic housing harms receive appropriate treatment.
The trap
Do not let low probability erase a potentially catastrophic consequence.

How to remember it

Prioritize high-consequence harms even when their likelihood is low.

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