The fairness of a deep neural network is strongly affected by dataset bias
and spurious correlations, both of which are usually present in modern
feature-rich and complex visual datasets. Due to the difficulty and variability
of the task, no single de-biasing method has been universally successful. In
particular, implicit methods not requiring explicit knowledge of bias variables
are especially relevant for real-world applications. We propose a novel
implicit mitigation method using a Bayesian neural network, allowing us to
leverage the relationship between epistemic uncertainties and the presence of
bias or spurious correlations in a sample. Our proposed posterior estimate
sharpening procedure encourages the network to focus on core features that do
not contribute to high uncertainties. Experimental results on three benchmark
datasets demonstrate that Bayesian networks with sharpened posterior estimates
perform comparably to prior existing methods and show potential worthy of
further exploration.

这篇论文提出了一种基于贝叶斯神经网络的去偏方法，通过刻画后验概率与数据偏差 (or 伪相关性) 之间的关系，利用高确信性的核心特征代替不确定性高的特征，从而提高了模型的公平性和准确性。