DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918115441377280 |
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| author | Guo, Yuhan Ding, Lizhong Jia, Shihan Ren, Yanyu Li, Pengqi Fu, Jiarun Li, Changsheng yuan, Ye Wang, Guoren |
| author_facet | Guo, Yuhan Ding, Lizhong Jia, Shihan Ren, Yanyu Li, Pengqi Fu, Jiarun Li, Changsheng yuan, Ye Wang, Guoren |
| contents | Explainable AI (XAI) builds trust in complex systems through model attribution methods that reveal the decision rationale. However, due to the absence of a unified optimal explanation, existing XAI methods lack a ground truth for objective evaluation and optimization. To address this issue, we propose Deep architecture-based Faith explainer (DeepFaith), a domain-free and model-agnostic unified explanation framework under the lens of faithfulness. By establishing a unified formulation for multiple widely used and well-validated faithfulness metrics, we derive an optimal explanation objective whose solution simultaneously achieves optimal faithfulness across these metrics, thereby providing a ground truth from a theoretical perspective. We design an explainer learning framework that leverages multiple existing explanation methods, applies deduplicating and filtering to construct high-quality supervised explanation signals, and optimizes both pattern consistency loss and local correlation to train a faithful explainer. Once trained, DeepFaith can generate highly faithful explanations through a single forward pass without accessing the model being explained. On 12 diverse explanation tasks spanning 6 models and 6 datasets, DeepFaith achieves the highest overall faithfulness across 10 metrics compared to all baseline methods, highlighting its effectiveness and cross-domain generalizability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03586 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations Guo, Yuhan Ding, Lizhong Jia, Shihan Ren, Yanyu Li, Pengqi Fu, Jiarun Li, Changsheng yuan, Ye Wang, Guoren Machine Learning Artificial Intelligence Explainable AI (XAI) builds trust in complex systems through model attribution methods that reveal the decision rationale. However, due to the absence of a unified optimal explanation, existing XAI methods lack a ground truth for objective evaluation and optimization. To address this issue, we propose Deep architecture-based Faith explainer (DeepFaith), a domain-free and model-agnostic unified explanation framework under the lens of faithfulness. By establishing a unified formulation for multiple widely used and well-validated faithfulness metrics, we derive an optimal explanation objective whose solution simultaneously achieves optimal faithfulness across these metrics, thereby providing a ground truth from a theoretical perspective. We design an explainer learning framework that leverages multiple existing explanation methods, applies deduplicating and filtering to construct high-quality supervised explanation signals, and optimizes both pattern consistency loss and local correlation to train a faithful explainer. Once trained, DeepFaith can generate highly faithful explanations through a single forward pass without accessing the model being explained. On 12 diverse explanation tasks spanning 6 models and 6 datasets, DeepFaith achieves the highest overall faithfulness across 10 metrics compared to all baseline methods, highlighting its effectiveness and cross-domain generalizability. |
| title | DeepFaith: A Domain-Free and Model-Agnostic Unified Framework for Highly Faithful Explanations |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2508.03586 |