Abstract: Thanks to the increasing growth of computational power and data availability,
the research in machine learning has advanced with tremendous rapidity.
Nowadays, the majority of automatic decision making systems are based on data.
However, it is well known that machine learning systems can present problematic
results if they are built on partial or incomplete data. In fact, in recent
years several studies have found a convergence of issues related to the ethics
and transparency of these systems in the process of data collection and how
they are recorded. Although the process of rigorous data collection and
analysis is fundamental in the model design, this step is still largely
overlooked by the machine learning community. For this reason, we propose a
method of data annotation based on Bayesian statistical inference that aims to
warn about the risk of discriminatory results of a given data set. In
particular, our method aims to deepen knowledge and promote awareness about the
sampling practices employed to create the training set, highlighting that the
probability of success or failure conditioned to a minority membership is given
by the structure of the data available. We empirically test our system on three
datasets commonly accessed by the machine learning community and we investigate
the risk of racial discrimination.