Principal Fairness for Human and Algorithmic Decision-Making

Understand

Using the concept of principal stratification from the causal inference literature, we introduce a new notion of fairness, called principal fairness, for human and algorithmic decision-making.

• The key idea is that one should not discriminate among individuals who would be similarly affected by the decision.
• Unlike the existing statistical definitions of fairness, principal fairness explicitly accounts for the fact that individuals can be impacted by the decision.
• Furthermore, we explain how principal fairness differs from the existing causality-based fairness criteria.