An argument paper reframes LLM explainability as an embodied, situated practice based on Dourish and enactivist cognition, identifying ontological obstacles in internal explanations and advocating affordance-based designs.
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications , shorttitle =
4 Pith papers cite this work. Polarity classification is still indexing.
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XtrAIn shifts occlusion from input space to parameter space along the training trajectory to produce cleaner feature attributions than standard methods.
Symb-xMIL is a post-hoc explanation framework that quantifies MIL model alignment with logical decision rules in histopathology to enable rule-based interpretability.
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
citing papers explorer
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Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology
Symb-xMIL is a post-hoc explanation framework that quantifies MIL model alignment with logical decision rules in histopathology to enable rule-based interpretability.