  Machine learning models are extensively being used to make decisions that
have a significant impact on human life. These models are trained over
historical data that may contain information about sensitive attributes such as
race, sex, religion, etc. The presence of such sensitive attributes can impact
certain population subgroups unfairly. It is straightforward to remove
sensitive features from the data; however, a model could pick up prejudice from
latent sensitive attributes that may exist in the training data. This has led
to the growing apprehension about the fairness of the employed models. In this
paper, we propose a novel algorithm that can effectively identify and treat
latent discriminating features. The approach is agnostic of the learning
algorithm and generalizes well for classification as well as regression tasks.
It can also be used as a key aid in proving that the model is free of
discrimination towards regulatory compliance if the need arises. The approach
helps to collect discrimination-free features that would improve the model
performance while ensuring the fairness of the model. The experimental results
from our evaluations on publicly available real-world datasets show a
near-ideal fairness measurement in comparison to other methods.
