A Comprehensive Guide to Explainable AI: From Classical Models to LLMs
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915052530958336 |
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| author | Hsieh, Weiche Bi, Ziqian Jiang, Chuanqi Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Xu, Jiawei Wang, Jinlang Chen, Keyu Feng, Pohsun Wen, Yizhu Song, Xinyuan Wang, Tianyang Liu, Ming Yang, Junjie Li, Ming Jing, Bowen Ren, Jintao Song, Junhao Tseng, Hong-Ming Zhang, Yichao Yan, Lawrence K. Q. Niu, Qian Chen, Silin Wang, Yunze Liang, Chia Xin |
| author_facet | Hsieh, Weiche Bi, Ziqian Jiang, Chuanqi Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Xu, Jiawei Wang, Jinlang Chen, Keyu Feng, Pohsun Wen, Yizhu Song, Xinyuan Wang, Tianyang Liu, Ming Yang, Junjie Li, Ming Jing, Bowen Ren, Jintao Song, Junhao Tseng, Hong-Ming Zhang, Yichao Yan, Lawrence K. Q. Niu, Qian Chen, Silin Wang, Yunze Liang, Chia Xin |
| contents | Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making processes. This book offers a comprehensive guide to XAI, bridging foundational concepts with advanced methodologies. It explores interpretability in traditional models such as Decision Trees, Linear Regression, and Support Vector Machines, alongside the challenges of explaining deep learning architectures like CNNs, RNNs, and Large Language Models (LLMs), including BERT, GPT, and T5. The book presents practical techniques such as SHAP, LIME, Grad-CAM, counterfactual explanations, and causal inference, supported by Python code examples for real-world applications.
Case studies illustrate XAI's role in healthcare, finance, and policymaking, demonstrating its impact on fairness and decision support. The book also covers evaluation metrics for explanation quality, an overview of cutting-edge XAI tools and frameworks, and emerging research directions, such as interpretability in federated learning and ethical AI considerations. Designed for a broad audience, this resource equips readers with the theoretical insights and practical skills needed to master XAI. Hands-on examples and additional resources are available at the companion GitHub repository: https://github.com/Echoslayer/XAI_From_Classical_Models_to_LLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00800 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Guide to Explainable AI: From Classical Models to LLMs Hsieh, Weiche Bi, Ziqian Jiang, Chuanqi Liu, Junyu Peng, Benji Zhang, Sen Pan, Xuanhe Xu, Jiawei Wang, Jinlang Chen, Keyu Feng, Pohsun Wen, Yizhu Song, Xinyuan Wang, Tianyang Liu, Ming Yang, Junjie Li, Ming Jing, Bowen Ren, Jintao Song, Junhao Tseng, Hong-Ming Zhang, Yichao Yan, Lawrence K. Q. Niu, Qian Chen, Silin Wang, Yunze Liang, Chia Xin Machine Learning Artificial Intelligence Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making processes. This book offers a comprehensive guide to XAI, bridging foundational concepts with advanced methodologies. It explores interpretability in traditional models such as Decision Trees, Linear Regression, and Support Vector Machines, alongside the challenges of explaining deep learning architectures like CNNs, RNNs, and Large Language Models (LLMs), including BERT, GPT, and T5. The book presents practical techniques such as SHAP, LIME, Grad-CAM, counterfactual explanations, and causal inference, supported by Python code examples for real-world applications. Case studies illustrate XAI's role in healthcare, finance, and policymaking, demonstrating its impact on fairness and decision support. The book also covers evaluation metrics for explanation quality, an overview of cutting-edge XAI tools and frameworks, and emerging research directions, such as interpretability in federated learning and ethical AI considerations. Designed for a broad audience, this resource equips readers with the theoretical insights and practical skills needed to master XAI. Hands-on examples and additional resources are available at the companion GitHub repository: https://github.com/Echoslayer/XAI_From_Classical_Models_to_LLMs. |
| title | A Comprehensive Guide to Explainable AI: From Classical Models to LLMs |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.00800 |