Fairness-aware training
Fairness constraints in machine learning are mathematical formulations that quantify and enforce specific notions of fairness by ensuring that model predictions maintain desired statistical properties across different demographic groups. These constraints typically express conditions such as demographic parity (equal positive prediction rates across groups), equalized odds (equal true positive and false positive rates), or individual fairness (similar individuals receive similar predictions). They can be incorporated directly into model optimization as regularization terms or enforced as post-processing steps. By explicitly modeling these constraints, developers can mitigate algorithmic bias and ensure more equitable outcomes across protected attributes like race, gender, or age—balancing the traditional goal of accuracy with ethical considerations about how predictive systems impact different populations.
Incorporating fairness constraints directly...