Demographic Parity Explained
The simplest fairness metric — and its limits.
Demographic parity (also called statistical parity) asks one question: do all groups receive the positive outcome at the same rate?
The math
For groups A and B:
P(prediction = positive | group A) ≈ P(prediction = positive | group B)If 30% of male applicants are shortlisted and 15% of female applicants are, the demographic parity difference is 15 percentage points and the disparate impact ratio is 0.5 — far below the 0.8 screening threshold.
When it's the right lens
- When the outcome allocates an opportunity or resource — interviews, loans, ad exposure for jobs and housing.
- When historical data is itself suspect: if past hiring was biased, "accurate" predictions reproduce that bias, and parity is the corrective lens.
Its blind spots
- It ignores qualifications entirely. A model can satisfy parity by selecting randomly within one group — equal rates, terrible decisions.
- If genuine base rates differ between groups, forcing parity can *create* unfairness at the individual level.
That's why the report pairs it with equalized odds, which conditions on the true outcome.
Practical guidance
Treat a parity gap as a flag to investigate, not an automatic verdict: check the input features, the training data's history, and whether a legitimate factor explains the gap — then document what you found.