  We propose an information-theoretic bias measurement technique through a
causal interpretation of spurious correlation, which is effective to identify
the feature-level algorithmic bias by taking advantage of conditional mutual
information. Although several bias measurement methods have been proposed and
widely investigated to achieve algorithmic fairness in various tasks such as
face recognition, their accuracy- or logit-based metrics are susceptible to
leading to trivial prediction score adjustment rather than fundamental bias
reduction. Hence, we design a novel debiasing framework against the algorithmic
bias, which incorporates a bias regularization loss derived by the proposed
information-theoretic bias measurement approach. In addition, we present a
simple yet effective unsupervised debiasing technique based on stochastic label
noise, which does not require the explicit supervision of bias information. The
proposed bias measurement and debiasing approaches are validated in diverse
realistic scenarios through extensive experiments on multiple standard
benchmarks.
