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.

翻译：过去的研究表明，在机器学习中显式使用受保护属性可以提升模型性能与公平性。然而，许多机器学习算法无法直接处理类别型属性（如出生地或族裔）。由于受保护属性通常为类别型，必须将其编码为所选机器学习算法（如支持向量机、梯度提升决策树或线性模型）可输入的特征。因此，编码方法会影响机器学习算法的学习方式与内容，进而影响模型性能与公平性。本研究比较了两种最著名的编码方法——独热编码与目标编码——在准确性和公平性方面的影响。我们将由这些编码方法可能引发的两种偏差类型加以区分：第一类为不可约偏差，源于直接的组间类别歧视；第二类为可约偏差，源于统计上代表性不足的群体中的高方差。我们探究了类别编码与降低不公平性的目标编码正则化方法之间的相互作用。此外，我们还考虑了当机器学习最佳实践通过将多个类别属性编码为高基数特征以提升性能指标时，可能产生的交叉不公平性问题。