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Rather than relying solely on formal definitions, researchers assess spuriousness through what we call pragmatic frames: Judgments based on what a correlation does in practice-how it affects model behavior, supports or impedes task performance, or aligns with broader norma","title":"The Pragmatic Frames of Spurious Correlations in Machine Learning: Interpreting How and Why They Matter","url":"https://arxiv.org/abs/2411.04696","vendor":"arxiv_cs_ai"},"summary":"arXiv:2411.04696v5 Announce Type: replace-cross \nAbstract: 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. 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