Pairwise Fairness for Ranking and Regression
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
Text
Narasimhan et al. "Pairwise Fairness for Ranking and Regression." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I04.5970Markdown
[Narasimhan et al. "Pairwise Fairness for Ranking and Regression." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/narasimhan2020aaai-pairwise/) doi:10.1609/AAAI.V34I04.5970BibTeX
@inproceedings{narasimhan2020aaai-pairwise,
title = {{Pairwise Fairness for Ranking and Regression}},
author = {Narasimhan, Harikrishna and Cotter, Andrew and Gupta, Maya R. and Wang, Serena Lutong},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {5248-5255},
doi = {10.1609/AAAI.V34I04.5970},
url = {https://mlanthology.org/aaai/2020/narasimhan2020aaai-pairwise/}
}