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Fair Generalized Linear Models with a Convex Penalty
Hyungrok Do · Preston Putzel · Axel Martin · Padhraic Smyth · Judy Zhong

Wed Jul 20 08:25 AM -- 08:30 AM (PDT) @ Room 318 - 320

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.

Author Information

Hyungrok Do (NYU Grossman School of Medicine)
Preston Putzel (University of California-Irvine)
Axel Martin (NYU Langone)
Padhraic Smyth (University of California, Irvine)
Judy Zhong (NYU School of Medicine)

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