Demographic Parity is the fairness criterion requiring that an AI system's positive prediction rate be equal across all protected demographic groups — meaning that the probability of receiving a favorable outcome (loan approval, job interview, ad shown) should be independent of sensitive attributes like race, gender, or age, regardless of whether the groups differ in their underlying qualification rates.
What Is Demographic Parity?
- Definition: A fairness metric satisfied when the probability of a positive prediction is equal across all demographic groups: P(Ŷ=1|A=a) = P(Ŷ=1|A=b) for all groups a, b.
- Alternative Names: Statistical parity, group fairness, independence criterion.
- Core Idea: If 30% of group A receives positive predictions, then 30% of group B should as well.
- Legal Connection: Related to the "four-fifths rule" in US employment law (adverse impact threshold).
Why Demographic Parity Matters
- Equal Opportunity Exposure: Ensures all groups have equal access to positive outcomes from AI systems.
- Historical Bias Correction: Prevents models from perpetuating historical discrimination encoded in training data.
- Legal Compliance: Closest fairness metric to legal concepts of disparate impact in employment and lending.
- Simple Interpretability: Easy to explain to non-technical stakeholders and regulators.
- Diversity Goals: Supports organizational diversity objectives in hiring and resource allocation.
How Demographic Parity Works
| Group | Total | Positive Predictions | Rate | DP Satisfied? |
|-------|-------|---------------------|------|--------------|
| Group A | 1000 | 300 | 30% | — |
| Group B | 1000 | 300 | 30% | ✓ Equal rates |
| Group A | 1000 | 300 | 30% | — |
| Group B | 1000 | 150 | 15% | ✗ Unequal rates |
Advantages
- Outcome Equality: Directly ensures equal positive outcome rates across groups.
- Measurable: Simple to compute and monitor in production systems.
- Proactive: Doesn't require ground truth labels — can be computed on predictions alone.
- Regulatory Alignment: Maps closely to legal fairness requirements.
Criticisms and Limitations
- Ignores Qualification: May require giving positive predictions to unqualified individuals to equalize rates.
- Accuracy Trade-Off: Enforcing equal rates when base rates differ necessarily reduces overall prediction accuracy.
- Incompatibility: Cannot be simultaneously satisfied with calibration when groups have different base rates (impossibility theorem).
- Laziness Risk: May be used as a checkbox without addressing underlying disparities.
- Context Sensitivity: Not appropriate for all applications — medical diagnosis should reflect actual disease prevalence.
When to Use Demographic Parity
- Advertising: Equal exposure to opportunities regardless of demographics.
- Hiring: Ensuring diverse candidate pools reach interview stages.
- Resource Allocation: Equal distribution of public resources across communities.
- Not recommended for: Medical diagnosis, risk assessment, or applications where base rate differences are clinically or scientifically meaningful.
Demographic Parity is the most intuitive and widely discussed fairness criterion — providing a clear, measurable standard for equal treatment in AI systems while acknowledging that its appropriateness depends critically on the application context and the values prioritized by stakeholders.