arXiv:2605.24084v1 Announce Type: new Abstract: Shapley additive explanations (SHAP) are widely recognised as computationally intractable for neural networks, since they induce an exponential search space over the input features. In this work, we take a first step towards scaling exact SHAP computation to larger search spaces by introducing an algorithm that leverages recent advances in neural network verification to compute arbitrarily tight exact lower and upper bounds on SHAP values for neural networks, ultimately recovering the exact SHAP values. We demonstrate that our approach scales to
The increasing complexity and opacity of neural networks necessitate more robust explainability techniques, and advancements in neural network verification are converging to address this need.
This breakthrough addresses a fundamental computational challenge in AI interpretability, making it possible to rigorously understand the decisions of complex models.
Exact and provable SHAP value computation for neural networks, previously considered intractable, becomes feasible for larger models, moving AI explainability from heuristic to verifiable.
- · AI researchers
- · AI developers
- · High-stakes AI applications (e.g., finance, medicine)
- · Regulatory bodies
- · Black-box AI systems with limited explainability
- · Heuristic explainability methods
The ability to obtain exact SHAP values will lead to more trustworthy and debuggable AI systems.
Increased trust and understanding could accelerate the adoption of AI in critical domains and influence regulatory frameworks for AI explainability.
Verifiable interpretability might become a standard requirement for AI deployment, potentially shaping future AI architecture design towards explainability.
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Read at arXiv cs.LG