arXiv AI
ICON Decomposition: Auditing Deep Neural Networks with Multivariate Variance-based Concept-level Explanations
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arXiv Machine Learning
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ICON Decomposition: Multivariate Concept-Level Explanations of Deep Representations for Model Auditing
ICON Decomposition is a new method for explaining deep neural networks by quantifying how much variance each concept explains in a network layer after accounting for all other concepts and the outcome. Unlike previous concept‑based methods that evaluate concepts in isolation, ICON can distinguish genuine model reliance from spurious correlations. Experiments on synthetic data, skin‑lesion, and brain‑imaging models show that ICON recovers concept importance more accurately, isolates truly relied‑upon concepts, and provides sparse explanations validated through retraining and out‑of‑distribution testing.
arXiv AI
2d ago
ICON Decomposition: Auditing deep neural networks for shortcuts by decomposing layer-wise representations using concepts
The paper introduces ICON Decomposition, a method for auditing deep neural networks by decomposing layer-wise representations into independent concept contributions. Unlike existing techniques that rely on linear probes or concept activation vectors, ICON quantifies the variance share each concept explains while conditioning on all other concepts and the outcome, allowing comparison across layers and concept types. Experiments on simulated data, skin‑cancer, and neuroimaging models show that ICON more accurately recovers true concept importance and can distinguish learned shortcuts from correlated concepts, as validated by retraining and out‑of‑distribution tests.
arXiv AI
Aug 3
A Human-Centered Validation of the Explainability-Performance Coefficient
arXiv:2607. 29614v1 Announce Type: cross Abstract: The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI).
arXiv AI
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Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
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arXiv AI
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ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI
arXiv:2607. 09649v1 Announce Type: new Abstract: Concept-based explainable artificial intelligence (AI) can make model reasoning more human-understandable, but concept-level outputs are not automatically trustworthy.
arXiv AI
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Understanding the Impact of Model Pruning on Long-Tail Forgetting and Explanation Reliability in Medical Imaging
arXiv:2609.07803v1 Announce Type: new Abstract: Model pruning is widely used to compress deep neural networks, reducing memory and computational requirements with minimal impact on aggregate performa...