Current research on bias in machine learning often focuses on fairness, while
overlooking the roots or causes of bias. However, bias was originally defined
as a "systematic error," often caused by humans at different stages of the
research process. This article aims to bridge the gap between past literature
on bias in research by providing taxonomy for potential sources of bias and
errors in data and models. The paper focus on bias in machine learning
pipelines. Survey analyses over forty potential sources of bias in the machine
learning (ML) pipeline, providing clear examples for each. By understanding the
sources and consequences of bias in machine learning, better methods can be
developed for its detecting and mitigating, leading to fairer, more
transparent, and more accurate ML models.

本文通过为潜在的偏见和数据模型中的错误提供分类法，目的是弥合过去关于偏见的文献以及在机器学习中缺少对其根源或原因的研究。调查分析了机器学习（ML）流程中超过四十个潜在的偏见来源，并且为每个来源提供了明确的示例。通过理解机器学习中偏见的来源和后果，可以开发出更好地检测和减轻偏见的方法，从而得到更公正、更透明和更准确的 ML 模型。