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.

翻译：广泛称为算法偏见的问题常出现在复杂的社会技术系统（STS）中，其中观察到的偏见可能不直接归因于单一的自动化决策算法。作为对社会技术系统中公平性的初步探究，我们聚焦于维基百科这一案例。我们系统性地回顾了75篇描述维基百科中不同类型偏见的论文，将这些偏见分类并与算法公平性研究中已确立的危害概念相联系。通过分析所观察现象之间的因果关系，我们展示了造成危害的社会技术过程的复杂性。最后，我们识别了与不同问题相关的公平性规范性期望，并讨论了为机器学习驱动决策系统提出的现有标准的适用性。