AI RESEARCH
The Pragmatic Frames of Spurious Correlations in Machine Learning: Interpreting How and Why They Matter
arXiv CS.LG
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ArXi:2411.04696v5 Announce Type: replace Learning correlations from data forms the foundation of today's machine learning (ML) and artificial intelligence research. While contemporary methods enable the automatic discovery of complex patterns, they are prone to failure when unintended correlations are captured. This vulnerability has spurred a growing interest in interrogating spuriousness, which is often seen as a threat to model performance, fairness, and robustness.