arXiv: cs.AI preprintsInternational6 November 2026
The Standard Interpretable Model: Design interpretable machine learning methods from elementary symmetries
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arXiv:2606.12289v3 Announce Type: replace-cross Abstract: As Artificial Intelligence models grow in complexity, interpretability has become an indispensable tool for understanding, debugging, and controlling their computations. However, interpretability lacks general theories to deductively design interpretable methods. This gap between theories and methods results in a fragmented literature and inconsistent evaluation protocols. To fill this gap, we introduce the Standard Interpretable Model (SIM), a general theory that enables the deductive design of interpretable methods from first principl
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