Bias in Machine Learning -- What is it Good for?

TL;DR

This paper provides a comprehensive taxonomy of the various meanings of bias in machine learning, analyzing their interrelations and implications for social discrimination and model fairness.

Contribution

It introduces a detailed taxonomy of bias types in machine learning and discusses their complex interconnections and impact on model fairness and social bias.

Findings

Bias in ML has multiple meanings and types.

The relationship between pipeline bias and model bias is complex.

Bias can have both positive and negative effects on fairness.

Abstract

In public media as well as in scientific publications, the term \emph{bias} is used in conjunction with machine learning in many different contexts, and with many different meanings. This paper proposes a taxonomy of these different meanings, terminology, and definitions by surveying the, primarily scientific, literature on machine learning. In some cases, we suggest extensions and modifications to promote a clear terminology and completeness. The survey is followed by an analysis and discussion on how different types of biases are connected and depend on each other. We conclude that there is a complex relation between bias occurring in the machine learning pipeline that leads to a model, and the eventual bias of the model (which is typically related to social discrimination). The former bias may or may not influence the latter, in a sometimes bad, and sometime good way.