  Several approaches have been developed to mitigate algorithmic bias stemming
from health data poverty, where minority groups are underrepresented in
training datasets. Augmenting the minority class using resampling (such as
SMOTE) is a widely used approach due to the simplicity of the algorithms.
However, these algorithms decrease data variability and may introduce
correlations between samples, giving rise to the use of generative approaches
based on GAN. Generation of high-dimensional, time-series, authentic data that
provides a wide distribution coverage of the real data, remains a challenging
task for both resampling and GAN-based approaches. In this work we propose
CA-GAN architecture that addresses some of the shortcomings of the current
approaches, where we provide a detailed comparison with both SMOTE and
WGAN-GP*, using a high-dimensional, time-series, real dataset of 3343
hypotensive Caucasian and Black patients. We show that our approach is better
at both generating authentic data of the minority class and remaining within
the original distribution of the real data.
