Yes, we have testing we could hand over
No rule fires on this answer, for a model that influences a decision reaching a consumer. A reviewer moves on.
Free snapshot · question 9 of 9
Could you show a carrier that your model does not disadvantage one group of people more than another?
This is usually called disparate impact testing or fairness testing. It means you compared your model's outcomes across groups of people, for example approval rates or average price by race, sex or age, and that you wrote down what you found.
Why a carrier asks
What the answer decides.
This is the question an insurance regulator is most associated with, and the one a carrier is least able to wave through. They are not asking whether your model is fair. They are asking whether you can hand them evidence, because without it the gap appears in their examination file rather than yours.
What the engine does with each answer
Derived, not written.
Each outcome below was produced by running Vaulith's rules engine on that answer, for a model that influences a decision reaching a consumer. The same rules run in the paid assessment, on your whole estate, with the arithmetic shown.
We have looked, but not in a sendable form
Will be questionedR-BIAS-STALE
Fairness testing is overdue for the system you described
Testing exists but the most recent record is more than two quarters old. Carrier standards and the model bulletin both expect a regular cadence on consumer-impacting systems, and a gap this size reads as a cadence that slipped rather than one that runs.
No, we have not looked at this
Will escalateR-NO-FAIRNESS-TESTING
No fairness testing on record for the system you described
Nothing shows whether the model produces different outcomes for different groups of people. This is the single question an insurance regulator is most associated with, so a carrier cannot simply note the absence and move on: they have to decide whether to accept it, and that decision gets made above the person reading your answers.
The examination items it touches
Where this lands in the questionnaire.
- AIS-5.1 Describe testing performed to detect unfair discrimination in AI system outputs, including methodology, protected classes considered, and cadence.NAIC AI model bulletin §4.4, unfair discrimination testing
- AIS-5.3 Provide the most recent testing results for each consumer-impacting system.NAIC AI model bulletin §4.4
- AIS-2.3 Describe the risk classification applied to each AI system and the criteria that place a system in the highest tier.NAIC AI Systems Evaluation Tool, high-risk model identification
Answer it about your own estate.
The snapshot asks this and eight more, in about ninety seconds, with nothing leaving your browser, and returns every finding with its reasoning and its remediation.