  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.
