Gradient Reversal against Discrimination: A Fair Neural Network Learning Approach

摘要

No methods currently exist for inducing fairness in arbitrary neural network architectures. In this work we introduce GRAD, a new and simplified method for producing fair neural networks that can be used for auto-encoding fair representations or directly with predictive networks. It is easy to implement and add to existing architectures, has only one (insensitive) hyper-parameter, and provides improved individual and group fairness. We use the flexibility of GRAD to demonstrate multi-attribute protection.