Spurious Correlation

Spurious correlation, the misleading association between features and labels unrelated to the true underlying relationship, is a significant challenge in machine learning, hindering model generalization and robustness. Current research focuses on mitigating spurious correlations in various model architectures, including deep neural networks and transformers, through techniques like data augmentation, feature re-weighting, and multi-objective optimization, often leveraging explainability methods to identify and address these biases. Understanding and addressing spurious correlations is crucial for building reliable and fair machine learning systems across diverse applications, from medical image analysis to natural language processing, impacting both the trustworthiness of AI and its equitable deployment.