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Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)
If meeting the requirement of explainability entails a reduction in system performance, then careful consideration must be given to which of these quality aspects takes precedence and how to compromise between them.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
The Power of Typed Affine Decision Structures: A Case Study
TADS are a novel, concise white-box representation of neural networks.
Towards Rigorous Understanding of Neural Networks via Semantics-preserving Transformations
In this paper we present an algebraic approach to the precise and global verification and explanation of Rectifier Neural Networks, a subclass of Piece-wise Linear Neural Networks (PLNNs), i. e., networks that semantically represent piece-wise affine functions.
