Gnothi Seauton: Empowering Faithful Self-Interpretability in Black-Box Models

• URL: http://arxiv.org/abs/2410.21815v1
• Date: Tue, 29 Oct 2024 07:35:33 GMT
• Title: Gnothi Seauton: Empowering Faithful Self-Interpretability in Black-Box Models
• Authors: Shaobo Wang, Hongxuan Tang, Mingyang Wang, Hongrui Zhang, Xuyang Liu, Weiya Li, Xuming Hu, Linfeng Zhang,
• Abstract summary: Self-interpretable models, such as concept-based networks, offer insights by connecting decisions to human-understandable concepts. Post-hoc methods like Shapley values, while theoretically robust, are computationally expensive and resource-intensive. We propose a novel method that combines their strengths, providing theoretically guaranteed self-interpretability for black-box models.
• Score: 21.698201509643624
• License:
• Abstract: The debate between self-interpretable models and post-hoc explanations for black-box models is central to Explainable AI (XAI). Self-interpretable models, such as concept-based networks, offer insights by connecting decisions to human-understandable concepts but often struggle with performance and scalability. Conversely, post-hoc methods like Shapley values, while theoretically robust, are computationally expensive and resource-intensive. To bridge the gap between these two lines of research, we propose a novel method that combines their strengths, providing theoretically guaranteed self-interpretability for black-box models without compromising prediction accuracy. Specifically, we introduce a parameter-efficient pipeline, *AutoGnothi*, which integrates a small side network into the black-box model, allowing it to generate Shapley value explanations without changing the original network parameters. This side-tuning approach significantly reduces memory, training, and inference costs, outperforming traditional parameter-efficient methods, where full fine-tuning serves as the optimal baseline. *AutoGnothi* enables the black-box model to predict and explain its predictions with minimal overhead. Extensive experiments show that *AutoGnothi* offers accurate explanations for both vision and language tasks, delivering superior computational efficiency with comparable interpretability.

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