Pairwise Fairness for Ranking and Regression
arXiv:1906.05330
Abstract
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.
References in corpus (8)
- Equality of Opportunity in Supervised Learning
- Counterfactual Fairness
- Fairness of Exposure in Rankings
- FA*IR: A Fair Top-k Ranking Algorithm
- Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
- A Convex Framework for Fair Regression
- Satisfying Real-world Goals with Dataset Constraints
- Policy Learning for Fairness in Ranking