Growing concerns regarding algorithmic fairness have led to a surge in methodologies to mitigate algorithmic bias. However, such methodologies largely assume that observed labels in training data are correct. This is problematic because bias in labels is pervasive across important domains, including healthcare, hiring, and content moderation. In particular, human-generated labels are prone to encoding societal biases. While the presence of labeling bias has been discussed conceptually, there is a lack of methodologies to address this problem. We propose a pruning method -- Decoupled Confident Learning (DeCoLe) -- specifically designed to mitigate label bias. After illustrating its performance on a synthetic dataset, we apply DeCoLe in the context of hate speech detection, where label bias has been recognized as an important challenge, and show that it successfully identifies biased labels and outperforms competing approaches.

翻译：对算法公平性的日益关注催生了大量缓解算法偏差的方法。然而，这些方法大多假设训练数据中的观测标签是正确的。这在医疗卫生、招聘和内容审核等重要领域中，因标签偏差普遍存在而成为问题。具体而言，人工生成的标签容易编码社会偏见。尽管标签偏差的存在已在概念层面得到讨论，但仍缺乏解决这一问题的具体方法。我们提出了一种剪枝方法——解耦置信学习（DeCoLe）——专门用于缓解标签偏差。在合成数据集上验证其性能后，我们将DeCoLe应用于仇恨言论检测场景（标签偏差在该领域已被视为重要挑战），结果表明该方法能成功识别偏差标签，并优于其他竞争性方法。