Fair Logistic Regression: An Adversarial Perspective

Fair prediction methods have primarily been built around existing classification techniques using In this paper we focus on group fairness measures, pre-processing methods, post-hoc adjustments, namely the three prevalent measures of demographic parity reduction-based constructions, or deep learning (Calders et al., 2009), equalized odds (Hardt et al., 2016), procedures. We investigate a new approach to and equalized opportunity (Hardt et al., 2016). Techniques fair data-driven decision making by designing for constructing predictors that provide these fairness guarantees predictors with fairness requirements integrated largely leverage existing classification methods as into their core formulations. We augment a black boxes. Preprocessing methods such as reweighting game-theoretic construction of the logistic regression and relabeling (Kamiran & Calders, 2012) transform model with fairness constraints, producing the input data to remove dependence between the class a novel prediction model that robustly and protected attribute according to a predefined fairness and fairly minimizes the logarithmic loss.