Group Annotation

Group annotation in machine learning focuses on improving model robustness and fairness by addressing biases stemming from spurious correlations and imbalanced data representation across subgroups. Current research emphasizes developing methods that mitigate these issues with minimal or no reliance on complete group annotations, employing techniques like self-supervised learning, robust optimization (e.g., worst-group loss minimization), and pseudo-labeling strategies to leverage partially annotated or unlabeled data. This work is significant because it enables the development of more reliable and equitable machine learning models, particularly in scenarios where comprehensive group annotations are costly, time-consuming, or ethically problematic.