Mitigating Label Bias in Machine Learning: Fairness through Confident Learning
This addresses fairness issues for underrepresented groups in ML by mitigating label bias, though it appears incremental as it builds on existing confident learning and co-teaching frameworks.
The paper tackles label bias in machine learning by proposing a method to filter fair instances using confident learning, truncation, and co-teaching, showing efficacy in promoting fairness and reducing bias impact across datasets.
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.