Learning Flexible and Fair Data Representations

doi:10.1109/ACCESS.2022.3207747

Learning Flexible and Fair Data Representations

2022-01-01

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DOI：10.1109/ACCESS.2022.3207747

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In this paper, we consider the problem of learning fair data representations that can be used for some downstream utility task in the vendor-user setting. We propose splitting the latent space between sensitive and non-sensitive latent variables where maximum mean discrepancy (MMD) is used to induce statistical independence between sensitive and non-sensitive latent variables. The non-sensitive latent representations can then be used for utility task by the user and achieve group and sub-group fairness with respect to multiple sensitive attributes. We perform extensive experiments and compare the proposed method against various representation learning methods proposed earlier in the literature. Our quantitative results and visualizations show that the proposed method produces representations that are able to achieve better or comparable performance at the utility task while simultaneously achieving sub-group and group fairness.

Keywords:

Task analysis

Representation learning

Machine learning

Decoding

Training data

Neural networks

Machine learning

representation learning

fairness

transparency

Learning Flexible and Fair Data Representations

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