arXiv:2606.01540v1 Announce Type: new Abstract: Shapley values are a widely used tool for attributing importance and interactions among input variables in black-box models, but their computation involves a function defined over an exponentially large space of subsets. We propose TN-SHAP-G, a framework that exploits structure in graph-structured inputs to compute Shapley values and higher-order interaction indices efficiently. Given a predictor and a fixed masking scheme, TN-SHAP-G learns a compact, graph-aligned multilinear surrogate that approximates the masked-input behavior, represented as
The increasing complexity and opacity of AI models necessitate improved interpretability tools for broader adoption and trust. This paper addresses a long-standing computational challenge in a critical area of AI interpretability.
Efficiently computing Shapley values for graph-structured data can unlock clearer understanding of complex AI systems, especially in domains like bioinformatics, social networks, and supply chain analysis. This advancement could accelerate the development and deployment of more transparent AI agents.
The computational burden of explaining graph-based AI model predictions is significantly reduced, enabling the routine use of Shapley values for explainability in complex, real-world graph data applications.
- · AI developers
- · Data scientists
- · Industries using graph AI (e.g., finance, healthcare)
- · AI ethics and safety researchers
- · Providers of less efficient interpretability solutions
- · Black-box AI models that resist explanation
More widespread adoption of explainable AI (XAI) techniques for graph-structured data models.
Increased trust and regulatory acceptance for AI systems operating on complex, interconnected data.
New research directions in AI interpretability focusing on higher-order interactions and model fidelity on graph structures.
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