AutoLIME and PWA-LIME: towards robust explanations of deep dynamical models
Year: 2025
Type: conference-paper
Abstract: The increasing complexity of machine learning models highlights the need for interpretability, especially in critical domains requiring trust and transparency. Local Interpretable Model-agnostic Explanations (LIME) is a popular eXplainable AI (XAI) method that provides localized, instance-specific explanations using an interpretable surrogate model. However, its effectiveness is limited by the lack of systematic guidelines for tuning its hyperparameters. This paper addresses this limitation by proposing Automatic LIME (AutoLIME), a bi-level optimization framework to tune LIME’s kernel width. Additionally, we introduce PieceWise Affine LIME (PWA-LIME), a clustering-based extension of LIME for multi-instance explanations, particularly useful for interpreting black-box models of dynamical systems. Preliminary numerical results validate the potential of these methods in explaining opaque dynamical models.
Institutions: Politecnico di Milano, Eindhoven University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7123364000
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