arXiv:2605.22738v1 Announce Type: new Abstract: Shapley and Banzhaf interactions capture the complex dynamics inherent in modern machine learning applications. However, current estimators for these higher-order interactions trade off between speed and accuracy. To overcome this limitation, we introduce ProxySHAP. ProxySHAP reconciles the high sample efficiency of tree-based proxy models with a principled path to consistency via residual correction. On a theoretical level, we derive a polynomial-time generalization of interventional TreeSHAP to compute exact interaction indices for tree ensembl
The proliferation of complex AI models necessitates more interpretable and robust methods for understanding their decisions, making current estimator limitations critical to address.
Improved methods for explainability like ProxySHAP enhance trust, regulatory compliance, and debugging capabilities in increasingly opaque AI systems, accelerating adoption in critical areas.
The ability to more efficiently and accurately approximate Shapley and Banzhaf interactions removes a barrier to deeply understanding complex AI model behavior, especially for tree ensembles.
- · AI developers
- · Machine learning researchers
- · Trustworthy AI initiatives
- · Sectors adopting complex AI
- · Black box AI solutions
- · Techniques relying on slow explainability models
Faster and more reliable explainability insights for complex AI models become widely available.
Increased adoption of AI in high-stakes environments due to enhanced transparency and debuggability.
New regulatory frameworks for AI explainability may emerge based on the availability of such advanced tools.
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Read at arXiv cs.LG