No methods currently exist for making arbitrary neural networks fair. In this work we introduce GRAD, a new and simplified method to producing fair neural networks that can be used for auto-encoding fair representations or directly with predictive networks. It is easy to implement and add to existing architectures, has only one (insensitive) hyper-parameter, and provides improved individual and group fairness. We use the flexibility of GRAD to demonstrate multi-attribute protection.
@article{arxiv.1807.00392,
title = {Gradient Reversal Against Discrimination},
author = {Edward Raff and Jared Sylvester},
journal= {arXiv preprint arXiv:1807.00392},
year = {2018}
}
Comments
Proceedings of the 5'th Workshop on Fairness, Accountability and Transparency in Machine Learning, 2018