Algorithmic Decision Making with Conditional Fairness
This work addresses fairness issues in decision-making systems for applications like hiring or lending, but it is incremental as it builds on existing fairness notions.
The paper tackles the problem of fairness in algorithmic decision-making by introducing conditional fairness, a metric that accounts for pre-decision covariates, and proposes a Derivable Conditional Fairness Regularizer (DCFR) to balance precision and fairness, showing advantages in experiments on three real-world datasets.
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.