Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization
This addresses fairness issues in image analysis applications, though it is an incremental improvement over existing bias mitigation techniques.
The paper tackles algorithmic bias in neural network classifiers by introducing a Wasserstein-2 regularization method that is architecture-agnostic and scalable, achieving good performance on benchmark datasets like Adult census, MNIST, and CelebA.
The increasingly common use of neural network classifiers in industrial and social applications of image analysis has allowed impressive progress these last years. Such methods are however sensitive to algorithmic bias, i.e. to an under- or an over-representation of positive predictions or to higher prediction errors in specific subgroups of images. We then introduce in this paper a new method to temper the algorithmic bias in Neural-Network based classifiers. Our method is Neural-Network architecture agnostic and scales well to massive training sets of images. It indeed only overloads the loss function with a Wasserstein-2 based regularization term for which we back-propagate the impact of specific output predictions using a new model, based on the Gateaux derivatives of the predictions distribution. This model is algorithmically reasonable and makes it possible to use our regularized loss with standard stochastic gradient-descent strategies. Its good behavior is assessed on the reference Adult census, MNIST, CelebA datasets.