2020
Fairness without Demographics through Adversarially Reweighted Learning
NeurIPS 2020poster
Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fairness concerns. However, in practice factors like privacy and regulation often preclude the collection of protected feat…