Pairwise Fairness for Ranking and Regression
Machine Learning
2020-01-08 v3 Machine Learning
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.
Cite
@article{arxiv.1906.05330,
title = {Pairwise Fairness for Ranking and Regression},
author = {Harikrishna Narasimhan and Andrew Cotter and Maya Gupta and Serena Wang},
journal= {arXiv preprint arXiv:1906.05330},
year = {2020}
}