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：深度神经网络的公正性受到数据集偏差和伪相关性的强烈影响，这两个因素通常存在于现代特征丰富且复杂的视觉数据集中。由于任务的难度和可变性，没有一种去偏方法是普遍成功的。特别是对于实际应用而言，不需要显式知识的隐式方法尤为重要。我们提出了一种使用贝叶斯神经网络的新型隐式缓解方法，允许我们利用随机不确定性与样本中偏差或伪相关性之间的关系。我们提出的后验估计锐化过程鼓励网络专注于不会导致高不确定性的核心特征。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神经网络的性能与先前存在的方法相当，并显示出值得进一步探索的潜力。