SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelism

arXiv — cs.LG•Monday, October 27, 2025 at 4:00:00 AM

A recent study has made significant strides in the field of machine learning by addressing the challenges of computing Shapley additive explanations (SHAP) for tensor networks, which are more complex than traditional models. This breakthrough is crucial because it allows for efficient and understandable explanations of black-box models like neural networks, enhancing transparency in AI systems. As AI continues to evolve, ensuring that these models can be interpreted is vital for trust and accountability in their applications.

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