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arXiv 2024年

作者： Wang, Shaobo Tang, Hongxuan Wang, Mingyang Zhang, Hongrui Liu, Xuyang Li, Weiya Hu, Xuming Zhang, Linfeng School of Artificial Intelligence Shanghai Jiao Tong University China Efficient and Precision Intelligent Computing Lab Shanghai Jiao Tong University China Sichuan University China Big Data and AI Lab ICBC Hong Kong University of Science and Technology Guangzhou China

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. © 2024, CC BY.

关键词： Economic and social effects

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