Machine learning systems are notoriously prone to biased predictions about certain demographic groups, leading to algorithmic fairness issues. Due to privacy concerns and data quality problems, some demographic information may not be available in the training data and the complex interaction of different demographics can lead to a lot of unknown minority subpopulations, which all limit the applicability of group fairness. Many existing works on fairness without demographics assume the correlation between groups and features. However, we argue that the model gradients are also valuable for fairness without demographics. In this paper, we show that the correlation between gradients and groups can help identify and improve group fairness. With an adversarial weighting architecture, we construct a graph where samples with similar gradients are connected and learn the weights of different samples from it. Unlike the surrogate grouping methods that cluster groups from features and labels as proxy sensitive attribute, our method leverages the graph structure as a soft grouping mechanism, which is much more robust to noises. The results show that our method is robust to noise and can improve fairness significantly without decreasing the overall accuracy too much.

翻译：机器学习系统因其对某些人口群体的预测存在偏见而臭名昭著，这导致了算法公平性问题。由于隐私担忧和数据质量问题，训练数据中可能无法获得某些人口统计信息，并且不同人口统计特征之间复杂的相互作用会产生大量未知的少数子群体，这些都限制了群体公平性的适用性。许多现有的无人口统计信息公平性研究假设了群体与特征之间的相关性。然而，我们认为模型梯度对于无人口统计信息的公平性同样具有价值。本文表明，梯度与群体之间的相关性有助于识别和改善群体公平性。通过一种对抗性加权架构，我们构建了一个图，其中梯度相似的样本相互连接，并从中学习不同样本的权重。与那些从特征和标签中聚类出群体作为代理敏感属性的替代分组方法不同，我们的方法利用图结构作为一种软分组机制，对噪声的鲁棒性更强。结果表明，我们的方法对噪声具有鲁棒性，并且能在不过度降低总体准确率的情况下显著改善公平性。