What Is Disparate Impact In Machine Learning?

Disparate Impact

Disparate impact is a quantitative measure of the adverse treatment of protected classes that compares the pass rate – or positive outcome – of one group versus another.


For example, a company with a fraud model might want to look at fairness of different groups. If disparate impact falls outside of the 0.8-1.25 range, it may mean that the sensitive group – such as people residing in zip codes where most of the population lives under the poverty line – is experiencing potentially discriminatory treatment. Clicking a level deeper might reveal disparate impact is the most pronounced for certain categories of purchases, such as for bail bonds, legal services, colleges, and even drugstore purchases.

Arize AI Bias Tracing Fairness Responsible AI Product Example

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