  Applications based on Machine Learning models have now become an
indispensable part of the everyday life and the professional world. A critical
question then recently arised among the population: Do algorithmic decisions
convey any type of discrimination against specific groups of population or
minorities? In this paper, we show the importance of understanding how a bias
can be introduced into automatic decisions. We first present a mathematical
framework for the fair learning problem, specifically in the binary
classification setting. We then propose to quantify the presence of bias by
using the standard Disparate Impact index on the real and well-known Adult
income data set. Finally, we check the performance of different approaches
aiming to reduce the bias in binary classification outcomes. Importantly, we
show that some intuitive methods are ineffective. This sheds light on the fact
trying to make fair machine learning models may be a particularly challenging
task, in particular when the training observations contain a bias.
