demographic parity
**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.