A differentiable adjusted parity loss based on soft balanced accuracy trains fair representations without adversarial components, improving demographic parity, equalized odds, and sensitive-feature accuracy on Adult and COMPAS datasets.
Wasserstein generative adversarial networks
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Differential Adjusted Parity for Learning Fair Representations
A differentiable adjusted parity loss based on soft balanced accuracy trains fair representations without adversarial components, improving demographic parity, equalized odds, and sensitive-feature accuracy on Adult and COMPAS datasets.