  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.
