The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics
arXiv:2606.12289v1 Announce Type: new 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 grounded in Lagrangian mechanics that enables the deductive design of interpretable meth
The increasing complexity of AI models has made interpretability a critical bottleneck, driving the urgent need for foundational theories to guide its development.
A general theory for interpretable AI could standardize design and evaluation, accelerating responsible AI development and deployment across various industries.
The systematic, deductive design of interpretable AI methods becomes possible, moving beyond ad-hoc solutions to a more unified and rigorous approach.
- · AI researchers
- · AI developers
- · Regulatory bodies
- · Industries deploying AI
- · Developers of ad-hoc interpretability methods
- · Companies relying on black-box AI
This paper establishes a theoretical framework for designing interpretable machine learning models, the Standard Interpretable Model (SIM).
The SIM could lead to the development of a new generation of inherently transparent AI systems with provable interpretability guarantees.
Greater trust and broader adoption of AI in high-stakes applications, potentially accelerating automation across critical sectors, while simultaneously creating new regulatory and auditing challenges.
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Read at arXiv cs.LG