A Canonical Data Transformation for Achieving Inter- and Within-group Fairness
This addresses fairness issues in sensitive applications like risk assessment, though it is incremental as it builds on existing group fairness methods.
The paper tackles the problem that group fairness methods can cause unfairness within groups, introducing a formal definition of within-group fairness and proposing a pre-processing framework to achieve both inter- and within-group fairness with minimal accuracy loss, applied to COMPAS and Law School datasets.
Increases in the deployment of machine learning algorithms for applications that deal with sensitive data have brought attention to the issue of fairness in machine learning. Many works have been devoted to applications that require different demographic groups to be treated fairly. However, algorithms that aim to satisfy inter-group fairness (also called group fairness) may inadvertently treat individuals within the same demographic group unfairly. To address this issue, we introduce a formal definition of within-group fairness that maintains fairness among individuals from within the same group. We propose a pre-processing framework to meet both inter- and within-group fairness criteria with little compromise in accuracy. The framework maps the feature vectors of members from different groups to an inter-group-fair canonical domain before feeding them into a scoring function. The mapping is constructed to preserve the relative relationship between the scores obtained from the unprocessed feature vectors of individuals from the same demographic group, guaranteeing within-group fairness. We apply this framework to the COMPAS risk assessment and Law School datasets and compare its performance in achieving inter-group and within-group fairness to two regularization-based methods.