  Learning data representations that are transferable and are fair with respect
to certain protected attributes is crucial to reducing unfair decisions while
preserving the utility of the data. We propose an information-theoretically
motivated objective for learning maximally expressive representations subject
to fairness constraints. We demonstrate that a range of existing approaches
optimize approximations to the Lagrangian dual of our objective. In contrast to
these existing approaches, our objective allows the user to control the
fairness of the representations by specifying limits on unfairness. Exploiting
duality, we introduce a method that optimizes the model parameters as well as
the expressiveness-fairness trade-off. Empirical evidence suggests that our
proposed method can balance the trade-off between multiple notions of fairness
and achieves higher expressiveness at a lower computational cost.
