A survey on bias in machine learning research

Abstract Current research on bias in machine learning often focuses on fairness, while overlooking the roots or causes of bias. Bias was originally defined as a ”system-atic error” often caused by humans at different stages of the research process. This paper aims to bridge the gap between past and present literature on bias in research by providing taxonomy for potential sources of bias and errors in data and models, with special focus paid 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 detection and mitigation, which lead to fairer, more transparent, and more accurate ML models.