Spurious Feature

Spurious features are attributes in data that correlate with target labels but lack a causal relationship, leading to unreliable and unfair machine learning models. Current research focuses on mitigating the impact of these features, employing techniques like contrastive learning, feature re-weighting, and attention mechanisms within various model architectures, including deep neural networks and large language models. Successfully addressing spurious features is crucial for improving the robustness, fairness, and generalizability of machine learning models across diverse applications, particularly in sensitive domains like facial recognition and medical diagnosis.