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：在复杂的社会技术系统(STS)中,经常出现被广泛称为算法偏差的问题,观察到的偏差可能不会直接归因于单一的自动决策算法。作为对STS中的公正性的第一次调查,我们把重点放在Wikipedia上。我们系统地审查75篇描述维基百科中不同类型偏差的论文,我们在Wikipedia中对这些论文进行了分类,并与从算法公平研究中形成的伤害概念有关。通过分析所观察到的现象之间的因果关系,我们显示了造成伤害的社会技术过程的复杂性。最后,我们确定了与不同问题相关的对公正性的规范性期望,并讨论了为机器学习驱动的决策系统提出的现有标准的适用性。
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