We propose a regression-based approach to removing implicit biases in representations. On tasks where the protected attribute is observed, the method is statistically more efficient than known approaches. Further, we show that this approach leads to debiased representations that satisfy a first order approximation of conditional parity. Finally, we demonstrate the efficacy of the proposed approach by reducing racial bias in recidivism risk scores.
@article{arxiv.1807.00461,
title = {Debiasing representations by removing unwanted variation due to protected attributes},
author = {Amanda Bower and Laura Niss and Yuekai Sun and Alexander Vargo},
journal= {arXiv preprint arXiv:1807.00461},
year = {2018}
}
Comments
Presented as a poster at the 2018 Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2018)