  A growing body of literature in fairness-aware machine learning (fairML) aims
to mitigate machine learning (ML)-related unfairness in automated
decision-making (ADM) by defining metrics that measure fairness of an ML model
and by proposing methods to ensure that trained ML models achieve low scores on
these metrics. However, the underlying concept of fairness, i.e., the question
of what fairness is, is rarely discussed, leaving a significant gap between
centuries of philosophical discussion and the recent adoption of the concept in
the ML community. In this work, we try to bridge this gap by formalizing a
consistent concept of fairness and by translating the philosophical
considerations into a formal framework for the training and evaluation of ML
models in ADM systems. We argue that fairness problems can arise even without
the presence of protected attributes (PAs), and point out that fairness and
predictive performance are not irreconcilable opposites, but that the latter is
necessary to achieve the former. Furthermore, we argue why and how causal
considerations are necessary when assessing fairness in the presence of PAs by
proposing a fictitious, normatively desired (FiND) world in which PAs have no
causal effects. In practice, this FiND world must be approximated by a warped
world in which the causal effects of the PAs are removed from the real-world
data. Finally, we achieve greater linguistic clarity in the discussion of
fairML. We outline algorithms for practical applications and present
illustrative experiments on COMPAS data.
