  Machine learning actively impacts our everyday life in almost all endeavors
and domains such as healthcare, finance, and energy. As our dependence on the
machine learning increases, it is inevitable that these algorithms will be used
to make decisions that will have a direct impact on the society spanning all
resolutions from personal choices to world-wide policies. Hence, it is crucial
to ensure that (un)intentional bias does not affect the machine learning
algorithms especially when they are required to take decisions that may have
unintended consequences. Algorithmic fairness techniques have found traction in
the machine learning community and many methods and metrics have been proposed
to ensure and evaluate fairness in algorithms and data collection.
  In this paper, we study the algorithmic fairness in a supervised learning
setting and examine the effect of optimizing a classifier for the Equal
Opportunity metric. We demonstrate that such a classifier has an increased
false positive rate across sensitive groups and propose a conceptually simple
method to mitigate this bias. We rigorously analyze the proposed method and
evaluate it on several real world datasets demonstrating its efficacy.
