Data Bias

Data bias, the presence of systematic errors in datasets that skew model outputs, is a critical concern across machine learning applications. Current research focuses on identifying and mitigating bias through various techniques, including counterfactual examples to improve data quality, Wasserstein barycenters for fairer risk assessment, and self-supervised adversarial training for robust model generalization. Addressing data bias is crucial for ensuring fairness, accuracy, and trustworthiness in machine learning models, impacting fields ranging from healthcare and finance to criminal justice and online security.