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Fair Learning with Private Demographic Data
[article]
2020
arXiv
pre-print
Sensitive attributes such as race are rarely available to learners in real world settings as their collection is often restricted by laws and regulations. We give a scheme that allows individuals to release their sensitive information privately while still allowing any downstream entity to learn non-discriminatory predictors. We show how to adapt non-discriminatory learners to work with privatized protected attributes giving theoretical guarantees on performance. Finally, we highlight how the
arXiv:2002.11651v2
fatcat:pca5cyxhdzcszm5lnopw6vvdmu