Just read 'Structural interpretability in SVMs with truncated orthogonal polynomial kernels' paper

The paper explores structural interpretability in SVMs using truncated orthogonal polynomial kernels. As a PM dealing with user trust issues, I'm wondering if these interpretable kernels could provide clearer explanations for AI decisions in production systems. Could this approach bridge the gap between model performance and user understanding in critical applications?