  Over the past several years, a slew of different methods to measure the
fairness of a machine learning model have been proposed. However, despite the
growing number of publications and implementations, there is still a critical
lack of literature that explains the interplay of fair machine learning with
the social sciences of philosophy, sociology, and law. We hope to remedy this
issue by accumulating and expounding upon the thoughts and discussions of fair
machine learning produced by both social and formal (specifically machine
learning and statistics) sciences in this field guide. Specifically, in
addition to giving the mathematical and algorithmic backgrounds of several
popular statistical and causal-based fair machine learning methods, we explain
the underlying philosophical and legal thoughts that support them. Further, we
explore several criticisms of the current approaches to fair machine learning
from sociological and philosophical viewpoints. It is our hope that this field
guide will help fair machine learning practitioners better understand how their
algorithms align with important humanistic values (such as fairness) and how we
can, as a field, design methods and metrics to better serve oppressed and
marginalized populaces.
