arXiv:2605.31200v1 Announce Type: new Abstract: Interpretable machine learning requires models that are accurate and structurally faithful to the data.Existing explainability methods rely heavily on additive representations (e.g., Generalized Additive Models (GAMs), SHapley Additive exPlanations (SHAP), functional ANOVA), which can suffer from signal cancellation and off-support extrapolation in the presence of strong interactions. We propose Tensor Separation Learning (TSL), a regression model that learns a sum of rank-1 products of univariate per-feature functions via a stagewise greedy proc
The increasing complexity and opacity of state-of-the-art AI models are driving the need for more robust and interpretable methodologies to ensure reliability and safety.
Improved interpretability methods like TSL are crucial for developing trustworthy AI, facilitating regulatory compliance, and enabling AI deployment in high-stakes environments.
The paradigm for achieving AI interpretability may shift from solely additive models to more sophisticated approaches that account for complex interactions, improving model faithfulness.
- · AI safety researchers
- · Healthcare AI
- · Financial AI
- · Regulatory bodies
- · Opaque black-box AI models
- · Traditional additive interpretability methods
AI models become more transparent, allowing for better debugging and understanding of their decision-making processes.
Increased trust in AI systems could accelerate their adoption in critical applications, reducing human oversight requirements for certain tasks.
New certification standards and regulations for AI could emerge, emphasizing interpretability as a core requirement for deployment.
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