  Bias in AI/ML-based systems is a ubiquitous problem and bias in AI/ML systems
may negatively impact society. There are many reasons behind a system being
biased. The bias can be due to the algorithm we are using for our problem or
may be due to the dataset we are using, having some features over-represented
in it. In the face detection system bias due to the dataset is majorly seen.
Sometimes models learn only features that are over-represented in data and
ignore rare features from data which results in being biased toward those
over-represented features. In real life, these biased systems are dangerous to
society. The proposed approach uses generative models which are best suited for
learning underlying features(latent variables) from the dataset and by using
these learned features models try to reduce the threats which are there due to
bias in the system. With the help of an algorithm, the bias present in the
dataset can be removed. And then we train models on two datasets and compare
the results.
