  Algorithmic fairness has attracted increasing attention in the machine
learning community. Various definitions are proposed in the literature, but the
differences and connections among them are not clearly addressed. In this
paper, we review and reflect on various fairness notions previously proposed in
machine learning literature, and make an attempt to draw connections to
arguments in moral and political philosophy, especially theories of justice. We
also consider fairness inquiries from a dynamic perspective, and further
consider the long-term impact that is induced by current prediction and
decision. In light of the differences in the characterized fairness, we present
a flowchart that encompasses implicit assumptions and expected outcomes of
different types of fairness inquiries on the data generating process, on the
predicted outcome, and on the induced impact, respectively. This paper
demonstrates the importance of matching the mission (which kind of fairness one
would like to enforce) and the means (which spectrum of fairness analysis is of
interest, what is the appropriate analyzing scheme) to fulfill the intended
purpose.

Machine Learning;Computers and Society