  Problems broadly known as algorithmic bias frequently occur in the context of
complex socio-technical systems (STS), where observed biases may not be
directly attributable to a single automated decision algorithm. As a first
investigation of fairness in STS, we focus on the case of Wikipedia. We
systematically review 75 papers describing different types of bias in
Wikipedia, which we classify and relate to established notions of harm from
algorithmic fairness research. By analysing causal relationships between the
observed phenomena, we demonstrate the complexity of the socio-technical
processes causing harm. Finally, we identify the normative expectations of
fairness associated with the different problems and discuss the applicability
of existing criteria proposed for machine learning-driven decision systems.
