  In the field of algorithmic fairness, many fairness criteria have been
proposed. Oftentimes, their proposal is only accompanied by a loose link to
ideas from moral philosophy -- which makes it difficult to understand when the
proposed criteria should be used to evaluate the fairness of a decision-making
system. More recently, researchers have thus retroactively tried to tie
existing fairness criteria to philosophical concepts. Group fairness criteria
have typically been linked to egalitarianism, a theory of distributive justice.
This makes it tempting to believe that fairness criteria mathematically
represent ideals of distributive justice and this is indeed how they are
typically portrayed. In this paper, we will discuss why the current approach of
linking algorithmic fairness and distributive justice is too simplistic and,
hence, insufficient. We argue that in the context of imperfect decision-making
systems -- which is what we deal with in algorithmic fairness -- we should not
only care about what the ideal distribution of benefits/harms among individuals
would look like but also about how deviations from said ideal are distributed.
Our claim is that algorithmic fairness is concerned with unfairness in these
deviations. This requires us to rethink the way in which we, as algorithmic
fairness researchers, view distributive justice and use fairness criteria.
