  Deep learning models often learn to make predictions that rely on sensitive
social attributes like gender and race, which poses significant fairness risks,
especially in societal applications, e.g., hiring, banking, and criminal
justice. Existing work tackles this issue by minimizing information about
social attributes in models for debiasing. However, the high correlation
between target task and social attributes makes bias mitigation incompatible
with target task accuracy. Recalling that model bias arises because the
learning of features in regard to bias attributes (i.e., bias features) helps
target task optimization, we explore the following research question: \emph{Can
we leverage proxy features to replace the role of bias feature in target task
optimization for debiasing?} To this end, we propose \emph{Proxy Debiasing}, to
first transfer the target task's learning of bias information from bias
features to artificial proxy features, and then employ causal intervention to
eliminate proxy features in inference. The key idea of \emph{Proxy Debiasing}
is to design controllable proxy features to on one hand replace bias features
in contributing to target task during the training stage, and on the other hand
easily to be removed by intervention during the inference stage. This
guarantees the elimination of bias features without affecting the target
information, thus addressing the fairness-accuracy paradox in previous
debiasing solutions. We apply \emph{Proxy Debiasing} to several benchmark
datasets, and achieve significant improvements over the state-of-the-art
debiasing methods in both of accuracy and fairness.
