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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.08789 |
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| _version_ | 1866918126381170688 |
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| author | Jiang, Yuchu Zhao, Jian Yuan, Yuchen Zhang, Tianle Huang, Yao Zhang, Yanghao Wang, Yan Li, Yanshu Guo, Xizhong Zhao, Yusheng Zhang, Jun Zhang, Zhi Lin, Xiaojian Zou, Yixiu Ma, Haoxuan Shang, Yuhu Hu, Yuzhi Cai, Keshu Zhang, Ruochen Chen, Boyuan Gao, Yilan Jiao, Ziheng Qin, Yi Du, Shuangjun Tong, Xiao Liu, Zhekun Chen, Yu Rong, Xuankun Wang, Rui Zheng, Yejie Fan, Zhaoxin Sensoy, Murat Zhang, Hongyuan Zhou, Pan Jin, Lei Zhao, Hao Yang, Xu Zhao, Jiaojiao Li, Jianshu Zhou, Joey Tianyi Cheng, Zhi-Qi Huang, Longtao Liu, Zhiyi Zhu, Zheng Li, Jianan Wang, Gang Li, Qi Zhang, Xu-Yao Yang, Yaodong Ye, Mang Ren, Wenqi He, Zhaofeng Su, Hang Ni, Rongrong Jing, Liping Wei, Xingxing Xing, Junliang Alioto, Massimo Shen, Shengmei Radeva, Petia Tao, Dacheng Zhang, Ya-Qin Yan, Shuicheng Zhang, Chi He, Zhongjiang Li, Xuelong |
| author_facet | Jiang, Yuchu Zhao, Jian Yuan, Yuchen Zhang, Tianle Huang, Yao Zhang, Yanghao Wang, Yan Li, Yanshu Guo, Xizhong Zhao, Yusheng Zhang, Jun Zhang, Zhi Lin, Xiaojian Zou, Yixiu Ma, Haoxuan Shang, Yuhu Hu, Yuzhi Cai, Keshu Zhang, Ruochen Chen, Boyuan Gao, Yilan Jiao, Ziheng Qin, Yi Du, Shuangjun Tong, Xiao Liu, Zhekun Chen, Yu Rong, Xuankun Wang, Rui Zheng, Yejie Fan, Zhaoxin Sensoy, Murat Zhang, Hongyuan Zhou, Pan Jin, Lei Zhao, Hao Yang, Xu Zhao, Jiaojiao Li, Jianshu Zhou, Joey Tianyi Cheng, Zhi-Qi Huang, Longtao Liu, Zhiyi Zhu, Zheng Li, Jianan Wang, Gang Li, Qi Zhang, Xu-Yao Yang, Yaodong Ye, Mang Ren, Wenqi He, Zhaofeng Su, Hang Ni, Rongrong Jing, Liping Wei, Xingxing Xing, Junliang Alioto, Massimo Shen, Shengmei Radeva, Petia Tao, Dacheng Zhang, Ya-Qin Yan, Shuicheng Zhang, Chi He, Zhongjiang Li, Xuelong |
| contents | The rapid advancement of AI has expanded its capabilities across domains, yet introduced critical technical vulnerabilities, such as algorithmic bias and adversarial sensitivity, that pose significant societal risks, including misinformation, inequity, security breaches, physical harm, and eroded public trust. These challenges highlight the urgent need for robust AI governance. We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security (system reliability), Derivative Security (real-world harm mitigation), and Social Ethics (value alignment and accountability). Uniquely, our approach unifies technical methods, emerging evaluation benchmarks, and policy insights to promote transparency, accountability, and trust in AI systems. Through a systematic review of over 300 studies, we identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight. These shortcomings stem from treating governance as an afterthought, rather than a foundational design principle, resulting in reactive, siloed efforts that fail to address the interdependence of technical integrity and societal trust. To overcome this, we present an integrated research agenda that bridges technical rigor with social responsibility. Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy. The accompanying repository is available at https://github.com/ZTianle/Awesome-AI-SG. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08789 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance Jiang, Yuchu Zhao, Jian Yuan, Yuchen Zhang, Tianle Huang, Yao Zhang, Yanghao Wang, Yan Li, Yanshu Guo, Xizhong Zhao, Yusheng Zhang, Jun Zhang, Zhi Lin, Xiaojian Zou, Yixiu Ma, Haoxuan Shang, Yuhu Hu, Yuzhi Cai, Keshu Zhang, Ruochen Chen, Boyuan Gao, Yilan Jiao, Ziheng Qin, Yi Du, Shuangjun Tong, Xiao Liu, Zhekun Chen, Yu Rong, Xuankun Wang, Rui Zheng, Yejie Fan, Zhaoxin Sensoy, Murat Zhang, Hongyuan Zhou, Pan Jin, Lei Zhao, Hao Yang, Xu Zhao, Jiaojiao Li, Jianshu Zhou, Joey Tianyi Cheng, Zhi-Qi Huang, Longtao Liu, Zhiyi Zhu, Zheng Li, Jianan Wang, Gang Li, Qi Zhang, Xu-Yao Yang, Yaodong Ye, Mang Ren, Wenqi He, Zhaofeng Su, Hang Ni, Rongrong Jing, Liping Wei, Xingxing Xing, Junliang Alioto, Massimo Shen, Shengmei Radeva, Petia Tao, Dacheng Zhang, Ya-Qin Yan, Shuicheng Zhang, Chi He, Zhongjiang Li, Xuelong Cryptography and Security The rapid advancement of AI has expanded its capabilities across domains, yet introduced critical technical vulnerabilities, such as algorithmic bias and adversarial sensitivity, that pose significant societal risks, including misinformation, inequity, security breaches, physical harm, and eroded public trust. These challenges highlight the urgent need for robust AI governance. We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security (system reliability), Derivative Security (real-world harm mitigation), and Social Ethics (value alignment and accountability). Uniquely, our approach unifies technical methods, emerging evaluation benchmarks, and policy insights to promote transparency, accountability, and trust in AI systems. Through a systematic review of over 300 studies, we identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight. These shortcomings stem from treating governance as an afterthought, rather than a foundational design principle, resulting in reactive, siloed efforts that fail to address the interdependence of technical integrity and societal trust. To overcome this, we present an integrated research agenda that bridges technical rigor with social responsibility. Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy. The accompanying repository is available at https://github.com/ZTianle/Awesome-AI-SG. |
| title | Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2508.08789 |