Missing values in real-world data pose a significant and unique challenge to
algorithmic fairness. Different demographic groups may be unequally affected by
missing data, and the standard procedure for handling missing values where
first data is imputed, then the imputed data is used for classification -- a
procedure referred to as "impute-then-classify" -- can exacerbate
discrimination. In this paper, we analyze how missing values affect algorithmic
fairness. We first prove that training a classifier from imputed data can
significantly worsen the achievable values of group fairness and average
accuracy. This is because imputing data results in the loss of the missing
pattern of the data, which often conveys information about the predictive
label. We present scalable and adaptive algorithms for fair classification with
missing values. These algorithms can be combined with any preexisting
fairness-intervention algorithm to handle all possible missing patterns while
preserving information encoded within the missing patterns. Numerical
experiments with state-of-the-art fairness interventions demonstrate that our
adaptive algorithms consistently achieve higher fairness and accuracy than
impute-then-classify across different datasets.

该论文研究了缺失数据对算法公平性的影响，并提出了一种可扩展和自适应的公平分类算法来处理所有可能的缺失模式，以保留缺失模式中编码的信息，并在多个数据集上实验证明该算法相比于传统的 impute-then-classify 方法在公平性和准确性上具有优势。