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.

翻译：深度神经网络的公平性受到数据集偏差和虚假相关性的强烈影响，这两者通常存在于现代特征丰富且复杂的视觉数据集中。由于该任务的难度和多样性，尚未有单一的偏差缓解方法能够普遍成功。特别是，无需显式了解偏差变量的隐式方法对于实际应用尤为重要。我们提出了一种新颖的隐式缓解方法，该方法使用贝叶斯神经网络，使我们能够利用样本中的认知不确定性与偏差或虚假相关性之间的关系。所提出的后验估计锐化程序鼓励网络专注于不导致高不确定性的核心特征。在三个基准数据集上的实验结果表明，具有锐化后验估计的贝叶斯网络与现有方法性能相当，并显示出值得进一步探索的潜力。