In public media as well as in scientific publications, the term \emph{bias} is used in conjunction with machine learning in many different contexts, and with many different meanings. This paper proposes a taxonomy of these different meanings, terminology, and definitions by surveying the, primarily scientific, literature on machine learning. In some cases, we suggest extensions and modifications to promote a clear terminology and completeness. The survey is followed by an analysis and discussion on how different types of biases are connected and depend on each other. We conclude that there is a complex relation between bias occurring in the machine learning pipeline that leads to a model, and the eventual bias of the model (which is typically related to social discrimination). The former bias may or may not influence the latter, in a sometimes bad, and sometime good way.

中文翻译：

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机器学习中的偏见——它有什么好处？

在公共媒体和科学出版物中，术语 \emph {bias} 在许多不同的上下文中与机器学习结合使用，并具有许多不同的含义。本文通过调查有关机器学习的主要科学文献，提出了这些不同含义、术语和定义的分类法。在某些情况下，我们建议扩展和修改以促进清晰的术语和完整性。调查之后会分析和讨论不同类型的偏见如何相互关联和相互依赖。我们得出结论，机器学习管道中出现的导致模型的偏差与模型的最终偏差（通常与社会歧视有关）之间存在复杂的关系。前者的偏见可能会也可能不会影响后者，