Disparate Impact
A policy or practice that appears neutral but produces disproportionately harmful outcomes for a protected class. A key test in insurance AI fairness review.
Disparate impact occurs when a policy or practice that seems neutral on its face produces disproportionately negative outcomes for members of a protected class. In insurance, this might show up as higher denial rates for one racial group, higher premiums for residents of certain neighborhoods, or worse claims outcomes for one gender.
The concept is central to AI fairness testing. Even if no protected characteristic is an explicit input, a model can produce disparate impact through proxy variables like geography, credit data, or occupation. Regulators in New York and under the NAIC Model Bulletin require carriers to test for these effects and document the results.
The standard response is not simply to remove the variable. It is to show either that the variable is not a proxy, or that it is required by a legitimate business need and no less-discriminatory alternative exists. See our guide to the proxy test.