Bias Correction

Bias correction in machine learning and related fields aims to mitigate systematic errors that skew model outputs or analyses, leading to inaccurate predictions or unfair outcomes. Current research focuses on developing and evaluating various bias correction techniques, including those based on propensity score matching, attention mechanisms in deep learning models, and generative adversarial networks, applied across diverse domains such as climate modeling, healthcare, and crime prediction. Effective bias correction is crucial for ensuring the reliability and fairness of machine learning models, improving the accuracy of scientific inferences, and promoting equitable applications in various sectors.