Post-hoc Bias Scoring Is Optimal For Fair Classification
This work addresses fairness in machine learning for applications requiring demographic parity, equalized opportunity, or equalized odds, offering a practical method for adapting existing classifiers.
The authors tackled the problem of binary classification under group fairness constraints by proposing a post-hoc approach that modifies an unconstrained classifier using a novel instance-level bias score, achieving optimal fairness while maintaining high accuracy.
We consider a binary classification problem under group fairness constraints, which can be one of Demographic Parity (DP), Equalized Opportunity (EOp), or Equalized Odds (EO). We propose an explicit characterization of Bayes optimal classifier under the fairness constraints, which turns out to be a simple modification rule of the unconstrained classifier. Namely, we introduce a novel instance-level measure of bias, which we call bias score, and the modification rule is a simple linear rule on top of the finite amount of bias scores.Based on this characterization, we develop a post-hoc approach that allows us to adapt to fairness constraints while maintaining high accuracy. In the case of DP and EOp constraints, the modification rule is thresholding a single bias score, while in the case of EO constraints we are required to fit a linear modification rule with 2 parameters. The method can also be applied for composite group-fairness criteria, such as ones involving several sensitive attributes.