arXiv:2510.22138v3 Announce Type: replace Abstract: We show how to replace the O(2^n) coalition enumeration over n features behind Shapley values and Shapley-style interaction indices with a few-evaluation scheme on a tensor-network (TN) surrogate: TN-SHAP. The key idea is to represent a predictor's local behavior as a factorized multilinear map, so that coalitional quantities become linear probes of a coefficient tensor. TN-SHAP replaces exhaustive coalition sweeps with just a small number of targeted evaluations to extract order-k Shapley interactions. In particular, both order-1 (single-fea
The increasing complexity of AI models and the critical need for explainability in high-stakes applications are driving the demand for more efficient methods to understand model behavior.
This development offers a potential breakthrough in making complex AI models more interpretable and robust, which is crucial for their adoption in sensitive or regulated domains.
The computational cost of understanding AI model feature attribution (Shapley values) is drastically reduced, enabling broader and more practical application in model debugging and compliance.
- · AI ethicists
- · Machine learning researchers
- · Developers of explainable AI tools
- · Sectors requiring high AI interpretability
- · Inefficient black-box AI models
- · Compute-constrained AI explainability methods
More widespread adoption of explainable AI techniques across various industries due to reduced computational burden.
Improved model trustworthiness and faster debugging cycles, accelerating AI development and deployment.
Potential for new regulatory frameworks to mandate or prefer AI models with verifiable explainability metrics like those enabled by TN-SHAP.
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Read at arXiv cs.LG