arXiv:2606.28065v1 Announce Type: new Abstract: Understanding model predictions is essential for physical applications, where outputs often inform safety-critical decisions, such as structural load assessment, weather warnings, and clinical diagnosis. Shapley values satisfy many desirable properties as an attribution method, but their computational cost during inference hinders their practical use. Current amortized explainers, such as FastSHAP, are limited to homogeneous inputs, which is problematic for physical applications where data often comes from irregular grids and geometries. We intro
The increasing complexity and deployment of AI in safety-critical physical applications necessitate improved interpretability methods, driving innovation in explainable AI like OperatorSHAP.
This development enhances the trustworthiness and practical applicability of AI in high-stakes fields by addressing limitations in current explainability techniques for complex, irregular data.
AI models can now be more reliably understood and certified for use in critical physical systems, even when dealing with non-standard data types, expanding AI's operational domain.
- · AI safety and certification bodies
- · Engineering and scientific AI users
- · Neural operator research
- · Black-box AI models in critical applications
- · Legacy explainability methods
Improved interpretability accelerates the adoption of AI in diverse, safety-critical engineering and scientific domains.
This could lead to new regulatory frameworks for AI explainability tailored to physical systems.
Increased trust in AI's foundational components could allow for more autonomous systems in infrastructure and defense, impacting national security and economic resilience.
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Read at arXiv cs.LG