BCPNNs are inherently interpretable models, and this work supplies a taxonomy, sixteen explanation primitives, and configuration artifacts to make their decisions auditable without post-hoc tools.
On pixel-wise explanations for non-linear classifier deci- sions by layer-wise relevance propagation
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Native Explainability for Bayesian Confidence Propagation Neural Networks: A Framework for Trusted Brain-Like AI
BCPNNs are inherently interpretable models, and this work supplies a taxonomy, sixteen explanation primitives, and configuration artifacts to make their decisions auditable without post-hoc tools.