Nowadays fairness issues have raised great concerns in decision-making
systems. Various fairness notions have been proposed to measure the degree to
which an algorithm is unfair. In practice, there frequently exist a certain set
of variables we term as fair variables, which are pre-decision covariates such
as users' choices. The effects of fair variables are irrelevant in assessing
the fairness of the decision support algorithm. We thus define conditional
fairness as a more sound fairness metric by conditioning on the fairness
variables. Given different prior knowledge of fair variables, we demonstrate
that traditional fairness notations, such as demographic parity and equalized
odds, are special cases of our conditional fairness notations. Moreover, we
propose a Derivable Conditional Fairness Regularizer (DCFR), which can be
integrated into any decision-making model, to track the trade-off between
precision and fairness of algorithmic decision making. Specifically, an
adversarial representation based conditional independence loss is proposed in
our DCFR to measure the degree of unfairness. With extensive experiments on
three real-world datasets, we demonstrate the advantages of our conditional
fairness notation and DCFR.

讨论了公平性问题，使用条款对算法进行了不公平度的度量，使用公平变量定义更声公平的公平模型，并提出了可嵌入任何决策模型的可导出条件公平正则化器（DCFR）。