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，而这些因素通常存在于当代特征丰富且复杂的视觉数据集中。由于任务的难度和多样性，目前尚无单一的去偏方法能普遍成功。特别是，无需明确了解偏差变量的隐式方法对实际应用尤为重要。我们提出了一种利用贝叶斯神经网络的新型隐式缓解方法，从而能够利用认知不确定性与样本中存在的偏差或虚假相关性之间的关系。我们提出的后验估计锐化过程鼓励网络聚焦于不会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，并展现出值得进一步探索的潜力。