Adaptation of XAI to Auto-tuning for Numerical Libraries

XAI is essential for validating AI in high-stakes computing: The paper highlights that manually inspecting AI-generated results for numerical computations is impractical at scale, making explainable AI a necessary tool for ensuring AI outputs can be trusted and validated before deployment in critical systems.

AI is increasingly used for performance optimization, but this creates transparency challenges: Auto-tuning technology uses AI to reduce manual work in optimizing numerical calculations, but integrating AI into these optimization processes makes it harder to understand why the AI made specific recommendations—a gap this research aims to address.

Explainability becomes more complex in specialized applications: The paper demonstrates that XAI techniques must be adapted differently depending on the specific use case, such as accuracy-guaranteed numerical calculations versus sparse iterative algorithms, suggesting that one-size-fits-all explainability solutions are insufficient.

Automation and visualization are critical for responsible AI deployment: The research emphasizes that because human oversight of all AI decisions is impractical, combining automated validation systems with clear visualizations of AI reasoning is necessary to maintain human oversight over AI-driven technical decisions.