Fair Generalized Linear Models with a Convex Penalty
This work addresses fairness in GLMs for practitioners in fields like healthcare or finance, offering a novel and efficient solution, though it is incremental as it builds on existing fairness methodologies.
The paper tackles the problem of achieving fairness in generalized linear models (GLMs), which are widely used but underexplored for fairness, by introducing two fairness criteria based on equalizing expected outcomes or log-likelihoods and proving they can be efficiently optimized via a convex penalty. The result is an empirically effective fair GLM that outperforms other methods on benchmark datasets for binary classification and regression and extends to various response variables.
Despite recent advances in algorithmic fairness, methodologies for achieving fairness with generalized linear models (GLMs) have yet to be explored in general, despite GLMs being widely used in practice. In this paper we introduce two fairness criteria for GLMs based on equalizing expected outcomes or log-likelihoods. We prove that for GLMs both criteria can be achieved via a convex penalty term based solely on the linear components of the GLM, thus permitting efficient optimization. We also derive theoretical properties for the resulting fair GLM estimator. To empirically demonstrate the efficacy of the proposed fair GLM, we compare it with other well-known fair prediction methods on an extensive set of benchmark datasets for binary classification and regression. In addition, we demonstrate that the fair GLM can generate fair predictions for a range of response variables, other than binary and continuous outcomes.