  The pipeline of a fair ML practitioner is generally divided into three
phases: 1) Selecting a fairness measure. 2) Choosing a model that minimizes
this measure. 3) Maximizing the model's performance on the data. In the context
of group fairness, this approach often obscures implicit assumptions about how
bias is introduced into the data. For instance, in binary classification, it is
often assumed that the best model, with equal fairness, is the one with better
performance. However, this belief already imposes specific properties on the
process that introduced bias. More precisely, we are already assuming that the
biasing process is a monotonic function of the fair scores, dependent solely on
the sensitive attribute. We formally prove this claim regarding several
implicit fairness assumptions. This leads, in our view, to two possible
conclusions: either the behavior of the biasing process is more complex than
mere monotonicity, which means we need to identify and reject our implicit
assumptions in order to develop models capable of tackling more complex
situations; or the bias introduced in the data behaves predictably, implying
that many of the developed models are superfluous.
