ICON Decomposition: Multivariate Concept-Level Explanations of Deep Representations for Model Auditing

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ICON Decomposition: Multivariate Concept-Level Explanations of Deep Representations for Model Auditing

Deep neural networks often exploit spurious associations in their training data, a failure known as shortcut learning. Concept-based explainability methods screen for shortcuts by testing whether concepts such as a patient's sex or scanner settings can be decoded from a network layer. Because each concept is evaluated in isolation, these methods can mistake correlations between concepts as evidence that the model uses them. We introduce ICON decomposition, which instead quantifies how much of a

Source: ArXiv AI