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：当带有潜在偏差的代理覆盖了原本无偏的标签时，歧视可能发生，导致数据集出现偏差，不公平地损害特定群体，并使得分类器继承这些偏差。在本文中，我们证明尽管只能获取到有偏标签，但通过可信学习框架内筛选最公平的实例，仍有可能消除偏差。在可信学习的背景下，低自置信度通常表示潜在的标签错误；然而，情况并非总是如此。特别是来自代表不足群体的实例，可能因标签错误之外的原因而表现出较低的置信度分数。为解决这一局限性，我们的方法采用置信度分数的截断，并扩展概率阈值的置信区间。此外，我们结合共同教学范式，以更稳健和可靠的方式选择公平实例，并有效缓解有偏标签的不利影响。通过对各种数据集的广泛实验和评估，我们证明了该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。