Achieving Fairness with a Simple Ridge Penalty

TL;DR

This paper introduces a straightforward ridge penalty framework to enforce fairness in regression models, allowing flexible fairness levels and extensions, with empirical evidence showing improved fit and accuracy over existing methods.

Contribution

It proposes a simple, interpretable, and extendable ridge penalty approach for fairness in regression, outperforming prior models in empirical evaluations.

Findings

Better goodness of fit and predictive accuracy at the same fairness level.

Mathematically simple with partly closed-form solutions.

Identifies bias in previous experimental evaluations.

Abstract

In this paper we present a general framework for estimating regression models subject to a user-defined level of fairness. We enforce fairness as a model selection step in which we choose the value of a ridge penalty to control the effect of sensitive attributes. We then estimate the parameters of the model conditional on the chosen penalty value. Our proposal is mathematically simple, with a solution that is partly in closed form, and produces estimates of the regression coefficients that are intuitive to interpret as a function of the level of fairness. Furthermore, it is easily extended to generalised linear models, kernelised regression models and other penalties; and it can accommodate multiple definitions of fairness. We compare our approach with the regression model from Komiyama et al. (2018), which implements a provably-optimal linear regression model; and with the fair models from Zafar et al. (2019). We evaluate these approaches empirically on six different data sets, and we find that our proposal provides better goodness of fit and better predictive accuracy for the same level of fairness. In addition, we highlight a source of bias in the original experimental evaluation in Komiyama et al. (2018).