A survey on bias in machine learning research
This is a survey paper that addresses bias in ML research, aiming to improve fairness, transparency, and accuracy for practitioners and researchers.
The paper tackles the problem of bias in machine learning by providing a taxonomy of over forty potential sources of bias in ML pipelines, with clear examples, to bridge gaps in existing literature that often overlook root causes.
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.