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We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes.
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A New Method for Estimating Race/Ethnicity and Associated Disparities Where Administrative Records Lack Self‐Reported Race/Ethnicity
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Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And it’s Biased Against Blacks., May 2016
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The variational fair autoencoder
Louizos, C., Swersky, K., Li, Y., Welling, M., and Zemel, R · 2016
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Understanding disentangling in β \beta -VAE
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Counterfactual fairness
Kusner, M. J., Loftus, J., Russell, C., and Silva, R · 2017
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On fairness and calibration
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Fixing a broken elbo
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Learning latent subspaces in variational autoencoders
Klys, J., Snell, J., and Zemel, R · 2018
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Learning adversarially fair and transferable representations
Madras, D., Creager, E., Pitassi, T., and Zemel, R · 2018
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Invariant representations without adversarial training
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Anchor regression: heterogeneous data meets causality
Rothenhäusler, D., Meinshausen, N., Bühlmann, P., and Peters, J · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T · 2018
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Excessive invariance causes adversarial vulnerability
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