Spurious Cue

Spurious cues, irrelevant features correlated with labels in training data, hinder the generalization ability of machine learning models, leading to inaccurate predictions on unseen data. Current research focuses on mitigating this issue through techniques like explanation-based fine-tuning, which encourages models to rely less on spurious correlations by requiring them to generate supporting explanations for their predictions, and by leveraging predictive uncertainty to identify and debias training samples. These efforts aim to improve model robustness and fairness, particularly in image classification, where spurious cues like backgrounds can significantly impact performance. Addressing spurious cues is crucial for building reliable and generalizable AI systems across various applications.