FairMixRep : Self-supervised Robust Representation Learning for Heterogeneous Data with Fairness constraints
This work addresses the need for fair and robust representation learning in domains with mixed data types, though it appears incremental by combining existing encoder-decoder and fairness constraint methods.
The authors tackled the problem of learning fair representations from heterogeneous data with mixed numerical and categorical variables in an unsupervised setting, achieving excellent performance in preserving information and fairness as validated by several metrics.
Representation Learning in a heterogeneous space with mixed variables of numerical and categorical types has interesting challenges due to its complex feature manifold. Moreover, feature learning in an unsupervised setup, without class labels and a suitable learning loss function, adds to the problem complexity. Further, the learned representation and subsequent predictions should not reflect discriminatory behavior towards certain sensitive groups or attributes. The proposed feature map should preserve maximum variations present in the data and needs to be fair with respect to the sensitive variables. We propose, in the first phase of our work, an efficient encoder-decoder framework to capture the mixed-domain information. The second phase of our work focuses on de-biasing the mixed space representations by adding relevant fairness constraints. This ensures minimal information loss between the representations before and after the fairness-preserving projections. Both the information content and the fairness aspect of the final representation learned has been validated through several metrics where it shows excellent performance. Our work (FairMixRep) addresses the problem of Mixed Space Fair Representation learning from an unsupervised perspective and learns a Universal representation that is timely, unique, and a novel research contribution.