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We consider the problem of learning representations that achieve group andsubgroup fairness with respect to multiple sensitive attributes. Takinginspiration from the disentangled representation learning literature, wepropose an algorithm for learning compact representations of datasets that areuseful for reconstruction and prediction, but are also \emph{flexibly fair},meaning they can be easily modified at test time to achieve subgroupdemographic parity with respect to multiple sensitive attributes and theirconjunctions. We show empirically that the resulting encoder---which does notrequire the sensitive attributes for inference---enables the adaptation of asingle representation to a variety of fair classification tasks with new targetlabels and subgroup definitions.

Flexibly Fair Representation Learning by Disentanglement