group fairness
What It Means
Group fairness means ensuring that an AI system treats different demographic groups equally in terms of outcomes or decisions. Instead of focusing on individual fairness, it looks at whether protected groups like different races, genders, or age brackets receive similar approval rates, error rates, or other measurable outcomes from the AI system.
Why Chief AI Officers Care
Failing to achieve group fairness can expose companies to discrimination lawsuits, regulatory penalties, and significant reputational damage. It's often a legal requirement under civil rights laws and increasingly scrutinized by regulators, investors, and customers who expect equitable AI systems.
Real-World Example
A bank's AI loan approval system shows that 80% of white applicants get approved but only 60% of Black applicants with similar credit profiles get approved. Even if the AI never explicitly considers race, this outcome disparity violates group fairness principles and could trigger legal action under fair lending laws.
Common Confusion
People often confuse group fairness with individual fairness - thinking that treating each person identically means the system is fair. However, identical treatment can still produce unequal outcomes across demographic groups due to historical biases embedded in training data.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, group fairness requires ensuring that diagnostic algorithms, treatment recommendations, and risk predi...
Finance: In finance, group fairness ensures that AI-driven lending, credit scoring, and insurance decisions don't systematically ...
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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 goal of groups defined by protected attributes receiving similar treatments or outcomes."Source: AI_Fairness_360
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