AINeutralarXiv – CS AI · Apr 156/10
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FaCT: Faithful Concept Traces for Explaining Neural Network Decisions
Researchers introduce FaCT, a new approach for explaining neural network decisions through faithful concept-based explanations that don't rely on restrictive assumptions about how models learn. The method includes a new evaluation metric (C²-Score) and demonstrates improved interpretability while maintaining competitive performance on ImageNet.