Bias Label

Bias in labeled data significantly impacts the fairness and accuracy of machine learning models, leading to research focused on mitigating this issue. Current efforts concentrate on developing algorithms and model architectures that identify and correct for bias, often employing techniques like pseudo-labeling refinement, logit adjustments, and contrastive learning, sometimes without requiring explicit bias annotations. This research is crucial for ensuring the reliability and ethical deployment of machine learning systems across various applications, particularly in sensitive domains like healthcare and criminal justice, where biased models can have severe real-world consequences.