  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.
