  In real world datasets, particular groups are under-represented, much rarer
than others, and machine learning classifiers will often preform worse on
under-represented populations. This problem is aggravated across many domains
where datasets are class imbalanced, with a minority class far rarer than the
majority class. Naive approaches to handle under-representation and class
imbalance include training sub-population specific classifiers that handle
class imbalance or training a global classifier that overlooks sub-population
disparities and aims to achieve high overall accuracy by handling class
imbalance. In this study, we find that these approaches are vulnerable in class
imbalanced datasets with minority sub-populations. We introduced Fair-Net, a
branched multitask neural network architecture that improves both
classification accuracy and probability calibration across identifiable
sub-populations in class imbalanced datasets. Fair-Nets is a straightforward
extension to the output layer and error function of a network, so can be
incorporated in far more complex architectures. Empirical studies with three
real world benchmark datasets demonstrate that Fair-Net improves classification
and calibration performance, substantially reducing performance disparity
between gender and racial sub-populations.
