Learning Controllable Fair Representations

Understand

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.