FairMask: Better Fairness via Model-Based Rebalancing of Protected Attributes

Context : Machine learning software can generate models that inappropriately discriminate against specific protected social groups (e.g., groups based on gender, ethnicity, etc.). Motivated by those results, software engineering researchers have proposed many methods for mitigating those discriminatory effects. While those methods are effective in mitigating bias, few of them can provide explanations on what is the root cause of bias. Objective : We aim to better detect and mitigate algorithmic discrimination in machine learning software problems. Method : Here we propose <tex-math notation="LaTeX">FairMask</tex-math> , a model-based extrapolation method that is capable of both mitigating bias and explaining the cause. In our <tex-math notation="LaTeX">FairMask</tex-math> approach, protected attributes are represented by models learned from the other independent variables (and these models offer extrapolations over the space between existing examples). We then use the extrapolation models to relabel protected attributes later seen in testing data or deployment time. Our approach aims to offset the biased predictions of the classification model by rebalancing the distribution of protected attributes. Results : The experiments of this paper show that, without compromising (original) model performance, <tex-math notation="LaTeX">FairMask</tex-math> can achieve significantly better group and individual fairness (as measured in different metrics) than benchmark methods. Moreover, compared to another instance-based rebalancing method, our model-based approach shows faster runtime and thus better scalability. Conclusion : Algorithmic decision bias can be removed via extrapolation that corrects the misleading latent correlation between the protected attributes and other non-protected ones. As evidence for this, our proposed <tex-math notation="LaTeX">FairMask</tex-math> is not only performance-wise better (measured by fairness and performance metrics) than two state-of-the-art fairness algorithms. Reproduction Package : In order to better support open science, all scripts and data used in this study are available online at <uri>https://github.com/anonymous12138/biasmitigation</uri> .