Learning Fair Classifiers with Partially Annotated Group Labels
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Recently, fairness-aware learning have become increasingly crucial, but we note that most of those methods operate by assuming the availability of fully annotated group-labels. We emphasize that such assumption is unrealistic for real-world applications since group label annotations are expensive and can conflict with privacy issues. In this paper, we consider a more practical scenario, dubbed as Algorithmic Fairness with the Partially annotated Group labels (Fair-PG). We observe that the existing fairness methods, which only use the data with group-labels, perform even worse than the vanilla training, which simply uses full data only with target labels, under Fair-PG. To address this problem, we propose a simple Confidence-based Group Label assignment (CGL) strategy that is readily applicable to any fairness-aware learning method. Our CGL utilizes an auxiliary group classifier to assign pseudo group labels, where random labels are assigned to low confident samples. We first theoretically show that our method design is better than the vanilla pseudo-labeling strategy in terms of fairness criteria. Then, we empirically show for UTKFace, CelebA and COMPAS datasets that by combining CGL and the state-of-the-art fairness-aware in-processing methods, the target accuracies and the fairness metrics are jointly improved compared to the baseline methods. Furthermore, we convincingly show that our CGL enables to naturally augment the given group-labeled dataset with external datasets only with target labels so that both accuracy and fairness metrics can be improved. We will release our implementation publicly to make future research reproduce our results.
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