Discrimination can occur when the underlying unbiased labels are overwritten by an agent with potential bias, resulting in biased datasets that unfairly harm specific groups and cause classifiers to inherit these biases. In this paper, we demonstrate that despite only having access to the biased labels, it is possible to eliminate bias by filtering the fairest instances within the framework of confident learning. In the context of confident learning, low self-confidence usually indicates potential label errors; however, this is not always the case. Instances, particularly those from underrepresented groups, might exhibit low confidence scores for reasons other than labeling errors. To address this limitation, our approach employs truncation of the confidence score and extends the confidence interval of the probabilistic threshold. Additionally, we incorporate with co-teaching paradigm for providing a more robust and reliable selection of fair instances and effectively mitigating the adverse effects of biased labels. Through extensive experimentation and evaluation of various datasets, we demonstrate the efficacy of our approach in promoting fairness and reducing the impact of label bias in machine learning models.

翻译：当潜在的未偏标签被具有潜在偏见的代理覆盖时，可能会发生歧视，导致数据集产生偏差，不公平地损害特定群体，并使分类器继承这些偏差。在本文中，我们证明，尽管只能访问有偏标签，但通过置信学习框架筛选最公平的实例，仍有可能消除偏差。在置信学习的背景下，低自信度通常表明潜在的标签错误；然而，情况并非总是如此。来自代表性不足群体的实例可能因标签错误以外的原因而表现低自信度。为解决这一限制，我们的方法采用自信度截断，并扩展概率阈值的置信区间。此外，我们结合共同教学范式，以更稳健可靠地选择公平实例，并有效减轻有偏标签的不利影响。通过广泛实验和多个数据集的评估，我们证明了我们的方法在促进公平性和减少机器学习模型中标签偏差影响方面的有效性。