@article{Foulds2020AnID,
title={An Intersectional Definition of Fairness},
author={James R. Foulds and Shimei Pan},
journal={2020 IEEE 36th International Conference on Data Engineering (ICDE)},
year={2020},
pages={1918-1921}
}

2020 IEEE 36th International Conference on Data Engineering (ICDE)

We propose differential fairness, a multi-attribute definition of fairness in machine learning which is informed by intersectionality, a critical lens arising from the humanities literature, leveraging connections between differential privacy and legal notions of fairness. We show that our criterion behaves sensibly for any subset of the set of protected attributes, and we prove economic, privacy, and generalization guarantees. We provide a learning algorithm which respects our differential… Expand

Code implementing differential fairness (DF) metric in: J. R. Foulds, R. Islam, K. Keya, and S. Pan. An Intersectional Definition of Fairness. 36th IEEE International Conference on Data Engineering (ICDE), 2020