Optimal Transport of Classifiers to Fairness
This addresses fairness in classifiers for applications where demographic parity is critical, offering a novel approach beyond simple rescaling methods.
The paper tackles the problem of fairness in machine learning by introducing Optimal Transport to Fairness (OTF), a method that quantifies unfairness as the smallest Optimal Transport cost between a classifier and any score function meeting fairness constraints, and experiments show it achieves an improved trade-off between predictive power and fairness.
In past work on fairness in machine learning, the focus has been on forcing the prediction of classifiers to have similar statistical properties for people of different demographics. To reduce the violation of these properties, fairness methods usually simply rescale the classifier scores, ignoring similarities and dissimilarities between members of different groups. Yet, we hypothesize that such information is relevant in quantifying the unfairness of a given classifier. To validate this hypothesis, we introduce Optimal Transport to Fairness (OTF), a method that quantifies the violation of fairness constraints as the smallest Optimal Transport cost between a probabilistic classifier and any score function that satisfies these constraints. For a flexible class of linear fairness constraints, we construct a practical way to compute OTF as a differentiable fairness regularizer that can be added to any standard classification setting. Experiments show that OTF can be used to achieve an improved trade-off between predictive power and fairness.