Spectral Model eXplainer: a chemically-grounded explainability framework for spectral-based machine learning models

Spectral AI needs domain-specific explainability tools: The paper demonstrates that general-purpose XAI methods like SHAP and SHAP were not designed for spectral data's unique characteristics (physical continuity and high collinearity), highlighting the importance of tailoring explainability frameworks to specific scientific domains rather than applying one-size-fits-all approaches.

Chemical meaning matters more than individual data points: SMX explains predictions through "chemically meaningful spectral zones" rather than isolated variables, recognizing that domain experts need explanations in their own language—in this case, interpretable regions of the spectrum—to trust and validate AI decisions in chemistry and materials science.

Explainability should be visually grounded in real measurements: A key innovation of SMX is "threshold spectrum reconstruction," which translates model explanations back into natural measurement units and visual spectra that scientists can directly compare with their actual data, making AI decisions verifiable against physical reality.

Responsible AI in science requires expert collaboration: The framework is explicitly "expert-informed," suggesting that building trustworthy AI systems for specialized fields requires close partnership between machine learning developers and domain scientists to ensure explanations are both technically sound and scientifically meaningful.

Evaluation across multiple real datasets strengthens credibility: Testing on eight real spectral datasets (primarily X-ray Fluorescence data) demonstrates the method's practical applicability rather than relying on toy examples, which is essential for responsible deployment in actual scientific and industrial settings.