🤖 AI Summary
This work addresses the problem of user-controllable fairness tuning in regression models. We propose a general fairness framework built upon ridge regression penalization. Its core innovation lies in explicitly incorporating fairness constraints into the ridge parameter selection process—achieving an adjustable trade-off between fairness and predictive performance via regularization with respect to sensitive attributes—and deriving partial closed-form solutions. The method supports multiple fairness definitions (e.g., demographic parity, equalized odds), extends to generalized linear models and kernelized settings, and corrects systematic experimental biases present in prior studies. Extensive experiments on six benchmark datasets demonstrate that, at comparable fairness levels, our approach significantly outperforms mainstream baselines—including Komiyama et al. and Zafar et al.—while simultaneously improving both goodness-of-fit and prediction accuracy.
📝 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. (in: Proceedings of machine learning research. 35th international conference on machine learning (ICML), vol 80, pp 2737–2746, 2018), which implements a provably optimal linear regression model and with the fair models from Zafar et al. (J Mach Learn Res 20:1–42, 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. (in: Proceedings of machine learning research. 35th international conference on machine learning (ICML), vol 80, pp 2737–2746, 2018).