arXiv:2606.09404v1 Announce Type: cross Abstract: Feature interactions drive much of the predictive power of machine learning models, yet existing explanation methods only detect and quantify interactions without revealing their functional form, or visualize only restricted interaction types. We propose Surrogate-based Analysis of Interactions via Local effect Smooths (SAILS), a model-agnostic framework that analyzes pairwise interactions through interpretable generalized additive model (GAM) surrogates fitted to the local effects of a black-box model. For each interval of a feature of interes
The proliferation of complex AI models necessitates advanced explainability techniques to understand their decision-making processes, particularly regarding feature interactions. This research addresses a critical gap in current AI explainability.
Improved understanding of how AI models exploit data interactions allows for better debugging, governance, and trust in AI systems, especially in high-stakes applications. This development facilitates the responsible deployment of increasingly powerful black-box models.
Machine learning practitioners will have a more nuanced tool for dissecting nonlinear relationships within their models, moving beyond simple interaction detection to visualize functional forms. This enables more precise model improvements and compliance efforts.
- · AI researchers
- · ML model developers
- · AI governance/compliance firms
- · Industries deploying black-box AI
- · Developers relying solely on superficial explainability methods
- · Companies with opaque proprietary models unable to articulate rationale
Wider adoption of advanced interaction analysis tools like SAILS for model interpretation and validation.
Increased ability to reverse-engineer and optimize the performance of complex AI models by understanding their interaction mechanisms.
Potential for new AI regulatory frameworks to mandate sophisticated interaction transparency, driven by the capabilities demonstrated by such tools.
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Read at arXiv cs.LG