Toward Faithfulness-guided Ensemble Interpretation of Neural Network

• URL: http://arxiv.org/abs/2509.04588v1
• Date: Thu, 04 Sep 2025 18:09:17 GMT
• Title: Toward Faithfulness-guided Ensemble Interpretation of Neural Network
• Authors: Siyu Zhang, Kenneth Mcmillan,
• Abstract summary: Interpretable and faithful explanations for specific neural inferences are crucial for understanding and evaluating model behavior.<n>Our work introduces textbfFaithfulness-guided textbfEnsemble textbfInterpretation (textbfFEI), an innovative framework that enhances the breadth and effectiveness of faithfulness.
• Score: 4.811608153296386
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interpretable and faithful explanations for specific neural inferences are crucial for understanding and evaluating model behavior. Our work introduces \textbf{F}aithfulness-guided \textbf{E}nsemble \textbf{I}nterpretation (\textbf{FEI}), an innovative framework that enhances the breadth and effectiveness of faithfulness, advancing interpretability by providing superior visualization. Through an analysis of existing evaluation benchmarks, \textbf{FEI} employs a smooth approximation to elevate quantitative faithfulness scores. Diverse variations of \textbf{FEI} target enhanced faithfulness in hidden layer encodings, expanding interpretability. Additionally, we propose a novel qualitative metric that assesses hidden layer faithfulness. In extensive experiments, \textbf{FEI} surpasses existing methods, demonstrating substantial advances in qualitative visualization and quantitative faithfulness scores. Our research establishes a comprehensive framework for elevating faithfulness in neural network explanations, emphasizing both breadth and precision

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