equality of odds
What It Means
Equality of odds means that an AI system makes mistakes at the same rate for different groups of people. If your hiring algorithm incorrectly rejects 10% of qualified white candidates, it should also incorrectly reject 10% of qualified Black candidates, and if it correctly accepts 80% of qualified white candidates, it should correctly accept 80% of qualified Black candidates.
Why Chief AI Officers Care
This metric is often required for regulatory compliance in hiring, lending, and healthcare applications where equal treatment across protected groups is legally mandated. Violating equality of odds can lead to discrimination lawsuits, regulatory fines, and reputational damage that can cost millions in settlements and lost business.
Real-World Example
A bank's loan approval AI system shows equality of odds when it correctly approves 85% of creditworthy applicants regardless of race, and incorrectly approves 5% of non-creditworthy applicants regardless of race. If the system correctly approved 90% of creditworthy white applicants but only 75% of creditworthy Black applicants with similar credit profiles, it would violate equality of odds.
Common Confusion
People often confuse equality of odds with overall approval rates being equal across groups, but equality of odds specifically requires that the accuracy rates are equal within each outcome category (qualified vs unqualified), not that each group gets approved at the same overall rate.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, equality of odds ensures that diagnostic or treatment recommendation systems have equal true positive ...
Finance: In credit scoring and lending, equality of odds requires that loan approval algorithms have identical true positive rate...
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- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"(Equalized odds). We say that a predictor bY satisfies equalized odds with respect to protected attribute A and outcome Y, if bY and A are independent conditional on Y."Source: hardt_equality_2016
"The probability of a person in the positive class being correctly assigned a positive outcome and the probability of a person in a negative class being incorrectly assigned a positive outcome should both be the same for the protected and unprotected group members. In other words, the protected and unprotected groups should have equal rates for true positives and false positives."Source: Mehrabi,_Ninareh
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