As machine learning (ML) increasingly affects people and society, awareness
of its potential unwanted consequences has also grown. To anticipate, prevent,
and mitigate undesirable downstream consequences, it is critical that we
understand when and how harm might be introduced throughou
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。