  With the rising adoption of Machine Learning across the domains like banking,
pharmaceutical, ed-tech, etc, it has become utmost important to adopt
responsible AI methods to ensure models are not unfairly discriminating against
any group. Given the lack of clean training data, generative adversarial
techniques are preferred to generate synthetic data with several
state-of-the-art architectures readily available across various domains from
unstructured data such as text, images to structured datasets modelling fraud
detection and many more. These techniques overcome several challenges such as
class imbalance, limited training data, restricted access to data due to
privacy issues. Existing work focusing on generating fair data either works for
a certain GAN architecture or is very difficult to tune across the GANs. In
this paper, we propose a pipeline to generate fairer synthetic data independent
of the GAN architecture. The proposed paper utilizes a pre-processing algorithm
to identify and remove bias inducing samples. In particular, we claim that
while generating synthetic data most GANs amplify bias present in the training
data but by removing these bias inducing samples, GANs essentially focuses more
on real informative samples. Our experimental evaluation on two open-source
datasets demonstrates how the proposed pipeline is generating fair data along
with improved performance in some cases.

Machine Learning;Artificial Intelligence;Computers and Society