Lesson 262 of 1550
Beyond Accuracy: Evaluating AI Classifiers for Fairness Across Subgroups
An AI classifier with 95% overall accuracy can have 70% accuracy for one demographic and 99% for another. Subgroup fairness evaluation is what catches this.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2subgroup analysis
- 3fairness metrics
- 4disparate impact
Concept cluster
Terms to connect while reading
Section 1
The premise
Aggregate accuracy hides demographic-specific failure modes; subgroup evaluation surfaces fairness issues before they harm users.
What AI does well here
- Define subgroups relevant to the use case (race, gender, age, geography, language, accessibility)
- Calculate accuracy + key error metrics per subgroup
- Choose appropriate fairness metrics (demographic parity, equal opportunity, calibration) based on use-case values
- Investigate causes when subgroups diverge (data representation, feature interactions, model behavior)
What AI cannot do
- Optimize all fairness metrics simultaneously (they often conflict)
- Substitute statistical fairness for substantive equity
- Eliminate the values judgments about which fairness definition matters
Key terms in this lesson
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