Gradient Reversal Against Discrimination
This addresses fairness issues in neural networks for applications requiring non-discriminatory predictions, but it appears incremental as it builds on existing fairness methods.
The authors tackled the problem of making arbitrary neural networks fair by introducing GRAD, a simplified method that improves both individual and group fairness with multi-attribute protection.
No methods currently exist for making arbitrary neural networks fair. In this work we introduce GRAD, a new and simplified method to 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.