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.

通过因果关系解释的虚假相关的条件互信息，我们提出了一种信息熵偏差测量技术，可以有效地识别基于特征的算法偏差，并设计了一种新颖的基于偏差正则化损失的去偏差框架和一种基于随机标签噪声的简单而有效的非监督去偏差技术。