statistical parity
What It Means
Statistical parity means that an AI system gives the same outcomes to different demographic groups at the same rate. For instance, if your hiring algorithm approves 60% of all applicants, it should approve roughly 60% of applicants from each racial group, gender, or other protected category.
Why Chief AI Officers Care
Failing to achieve statistical parity can trigger discrimination lawsuits, regulatory investigations, and damage to brand reputation when bias becomes public. Many jurisdictions now require algorithmic auditing that specifically tests for statistical parity, making it a compliance requirement rather than just an ethical consideration.
Real-World Example
A bank's loan approval algorithm shows statistical parity if it approves 75% of white applicants and also approves 75% of Black applicants and 75% of Hispanic applicants. If the approval rates are 75%, 45%, and 50% respectively, the system violates statistical parity even if the algorithm never directly considers race.
Common Confusion
People often confuse statistical parity with 'fairness' broadly, but achieving equal approval rates doesn't necessarily mean the system is making quality decisions or treating individuals fairly based on their actual qualifications and circumstances.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, statistical parity requires that diagnostic algorithms, treatment recommendations, or risk assessment ...
Finance: In finance, statistical parity ensures that AI-driven decisions like loan approvals, credit scoring, or insurance pricin...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"The independence between the protected attribute and the outcome of the decision rule"Source: Besse,_Philippe
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