A method to reduce algorithmic bias in neural-network classifiers by regularizing with the Wasserstein-2 distance between score distributions of two groups, with approximate gradients that fit into SGD.
In: Proceedings of the 27th International Conference on Neural Information Processing Systems - V olume 2, p
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Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularization
A method to reduce algorithmic bias in neural-network classifiers by regularizing with the Wasserstein-2 distance between score distributions of two groups, with approximate gradients that fit into SGD.