  Past research has demonstrated that the explicit use of protected attributes
in machine learning can improve both performance and fairness. Many machine
learning algorithms, however, cannot directly process categorical attributes,
such as country of birth or ethnicity. Because protected attributes frequently
are categorical, they must be encoded as features that can be input to a chosen
machine learning algorithm, e.g.\ support vector machines, gradient boosting
decision trees or linear models. Thereby, encoding methods influence how and
what the machine learning algorithm will learn, affecting model performance and
fairness. This work compares the accuracy and fairness implications of the two
most well-known encoding methods: \emph{one-hot encoding} and \emph{target
encoding}. We distinguish between two types of induced bias that may arise from
these encoding methods and may lead to unfair models. The first type,
\textit{irreducible bias}, is due to direct group category discrimination, and
the second type, \textit{reducible bias}, is due to the large variance in
statistically underrepresented groups. We investigate the interaction between
categorical encodings and target encoding regularization methods that reduce
unfairness. Furthermore, we consider the problem of intersectional unfairness
that may arise when machine learning best practices improve performance
measures by encoding several categorical attributes into a high-cardinality
feature.
