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Machine learning models often learn to make predictions that rely onsensitive social attributes like gender and race, which poses significantfairness risks, especially in societal applications, such as hiring, banking,and criminal justice. Existing work tackles this issue by minimizing theemployed information about social attributes in models for debiasing. However,the high correlation between target task and these social attributes makeslearning on the target task incompatible with debiasing. Given that model biasarises due to the learning of bias features (\emph{i.e}., gender) that helptarget task optimization, we explore the following research question: \emph{Canwe leverage shortcut features to replace the role of bias feature in targettask optimization for debiasing?} To this end, we propose \emph{ShortcutDebiasing}, to first transfer the target task's learning of bias attributesfrom bias features to shortcut features, and then employ causal intervention toeliminate shortcut features during inference. The key idea of \emph{ShortcutDebiasing} is to design controllable shortcut features to on one hand replacebias features in contributing to the target task during the training stage, andon the other hand be easily removed by intervention during the inference stage.This guarantees the learning of the target task does not hinder the eliminationof bias features. We apply \emph{Shortcut Debiasing} to several benchmarkdatasets, and achieve significant improvements over the state-of-the-artdebiasing methods in both accuracy and fairness.

Benign Shortcut for Debiasing: Fair Visual Recognition via Intervention with Shortcut Features