Spurious Attribute

Spurious attributes, features correlated with a target variable but not causally related, lead to unreliable machine learning models that perform poorly on subgroups lacking these correlations. Current research focuses on developing methods to mitigate this issue, even without explicit knowledge of the spurious attributes, employing techniques like loss-based resampling, logit correction, and contrastive learning to improve model robustness and worst-group accuracy. These advancements are crucial for building more reliable and generalizable AI systems, particularly in applications where fairness and robustness across diverse subgroups are paramount.