Biased Training

Biased training in machine learning refers to the phenomenon where models learn spurious correlations from skewed or unrepresentative training data, leading to unfair or inaccurate predictions for certain subgroups. Current research focuses on mitigating this bias through various techniques, including data augmentation with synthetic samples, developing bias-aware training algorithms (e.g., those adjusting sample weights or employing contrastive learning), and post-hoc methods that modify model predictions or architectures to improve fairness. Addressing biased training is crucial for ensuring the reliability and ethical deployment of machine learning models across diverse applications, particularly in sensitive domains like healthcare and finance.