Pairwise Fairness for Ranking and Regression
This work addresses fairness issues in machine learning for ranking and regression tasks, but it is incremental as it adapts existing methods from fair classification.
The authors tackled the problem of fairness in ranking and regression models by introducing pairwise fairness metrics analogous to statistical fairness notions like equal opportunity, and showed that these can be efficiently solved using existing constrained optimization techniques, with experiments demonstrating broad applicability and trade-offs.
We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity. Our pairwise formulation supports both discrete protected groups, and continuous protected attributes. We show that the resulting training problems can be efficiently and effectively solved using existing constrained optimization and robust optimization techniques developed for fair classification. Experiments illustrate the broad applicability and trade-offs of these methods.