Fairness Metrics: Demographic Parity, Equalized Odds, and Trade-offs › Summary

Fairness metrics translate ethical intuitions about equitable treatment into measurable mathematical quantities. The key concepts covered in this chapter are:

• Demographic parity: positive prediction rates are equal across groups, regardless of true outcomes. Simple to measure and does not require conditioning on labels; ignores whether the labels themselves are biased.
• Equalized odds: both TPR and FPR are equal across groups. Provides symmetric protection for both positive and negative class individuals; requires reliable ground truth labels.
• Equal opportunity: only TPR is equal across groups. A targeted protection for the positive class, appropriate when false negatives are the primary harm.
• Predictive parity: precision is equal across groups. Ensures the model's positive predictions are equally reliable; favors the institution making decisions over the individuals being classified.
• Individual fairness: similar individuals are treated similarly according to a task-specific similarity metric. Requires defining what "similar" means, which is a domain-specific problem.
• Counterfactual fairness: predictions do not change when a person's sensitive attribute is counterfactually changed while holding causally unrelated factors constant. Requires a causal model of feature generation.
• Calibration within groups: risk scores mean the same thing for all demographic groups. A sufficient condition for predictive parity and a foundation for trustworthy probabilistic predictions.

The most important theoretical result is the impossibility theorem: when base rates differ across groups, no imperfect classifier can simultaneously satisfy equalized odds and predictive parity. This forces a deliberate choice about which criterion matters most for your application.

Choosing among fairness metrics requires thinking carefully about: the costs of different error types and who bears them, whether ground truth labels are reliable and unbiased, intersectional subgroups that may be hidden in aggregate statistics, and the legal and regulatory context.

In the next chapter, we explore representation harms, a different dimension of fairness that concerns classification decisions and the ways language models encode and reproduce stereotypes and erasure in their outputs.