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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.14296 |
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| _version_ | 1866913131140218880 |
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| author | Huang, Yue Gao, Chujie Wu, Siyuan Wang, Haoran Wang, Xiangqi Zhou, Yujun Wang, Yanbo Ye, Jiayi Shi, Jiawen Zhang, Qihui Li, Yuan Bao, Han Liu, Zhaoyi Guan, Tianrui Chen, Dongping Chen, Ruoxi Guo, Kehan Zou, Andy Kuen-Yew, Bryan Hooi Xiong, Caiming Stengel-Eskin, Elias Zhang, Hongyang Yin, Hongzhi Zhang, Huan Yao, Huaxiu Yoon, Jaehong Zhang, Jieyu Shu, Kai Zhu, Kaijie Krishna, Ranjay Swayamdipta, Swabha Shi, Taiwei Shi, Weijia Li, Xiang Li, Yiwei Hao, Yuexing Jia, Zhihao Li, Zhize Chen, Xiuying Tu, Zhengzhong Hu, Xiyang Zhou, Tianyi Zhao, Jieyu Sun, Lichao Huang, Furong Sasson, Or Cohen Sattigeri, Prasanna Reuel, Anka Lamparth, Max Zhao, Yue Dziri, Nouha Su, Yu Sun, Huan Ji, Heng Xiao, Chaowei Bansal, Mohit Chawla, Nitesh V. Pei, Jian Gao, Jianfeng Backes, Michael Yu, Philip S. Gong, Neil Zhenqiang Chen, Pin-Yu Li, Bo Song, Dawn Zhang, Xiangliang |
| author_facet | Huang, Yue Gao, Chujie Wu, Siyuan Wang, Haoran Wang, Xiangqi Zhou, Yujun Wang, Yanbo Ye, Jiayi Shi, Jiawen Zhang, Qihui Li, Yuan Bao, Han Liu, Zhaoyi Guan, Tianrui Chen, Dongping Chen, Ruoxi Guo, Kehan Zou, Andy Kuen-Yew, Bryan Hooi Xiong, Caiming Stengel-Eskin, Elias Zhang, Hongyang Yin, Hongzhi Zhang, Huan Yao, Huaxiu Yoon, Jaehong Zhang, Jieyu Shu, Kai Zhu, Kaijie Krishna, Ranjay Swayamdipta, Swabha Shi, Taiwei Shi, Weijia Li, Xiang Li, Yiwei Hao, Yuexing Jia, Zhihao Li, Zhize Chen, Xiuying Tu, Zhengzhong Hu, Xiyang Zhou, Tianyi Zhao, Jieyu Sun, Lichao Huang, Furong Sasson, Or Cohen Sattigeri, Prasanna Reuel, Anka Lamparth, Max Zhao, Yue Dziri, Nouha Su, Yu Sun, Huan Ji, Heng Xiao, Chaowei Bansal, Mohit Chawla, Nitesh V. Pei, Jian Gao, Jianfeng Backes, Michael Yu, Philip S. Gong, Neil Zhenqiang Chen, Pin-Yu Li, Bo Song, Dawn Zhang, Xiangliang |
| contents | Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions. This paper presents a comprehensive framework to address these challenges through three key contributions. First, we systematically review global AI governance laws and policies from governments and regulatory bodies, as well as industry practices and standards. Based on this analysis, we propose a set of guiding principles for GenFMs, developed through extensive multidisciplinary collaboration that integrates technical, ethical, legal, and societal perspectives. Second, we introduce TrustGen, the first dynamic benchmarking platform designed to evaluate trustworthiness across multiple dimensions and model types, including text-to-image, large language, and vision-language models. TrustGen leverages modular components--metadata curation, test case generation, and contextual variation--to enable adaptive and iterative assessments, overcoming the limitations of static evaluation methods. Using TrustGen, we reveal significant progress in trustworthiness while identifying persistent challenges. Finally, we provide an in-depth discussion of the challenges and future directions for trustworthy GenFMs, which reveals the complex, evolving nature of trustworthiness, highlighting the nuanced trade-offs between utility and trustworthiness, and consideration for various downstream applications, identifying persistent challenges and providing a strategic roadmap for future research. This work establishes a holistic framework for advancing trustworthiness in GenAI, paving the way for safer and more responsible integration of GenFMs into critical applications. To facilitate advancement in the community, we release the toolkit for dynamic evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14296 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective Huang, Yue Gao, Chujie Wu, Siyuan Wang, Haoran Wang, Xiangqi Zhou, Yujun Wang, Yanbo Ye, Jiayi Shi, Jiawen Zhang, Qihui Li, Yuan Bao, Han Liu, Zhaoyi Guan, Tianrui Chen, Dongping Chen, Ruoxi Guo, Kehan Zou, Andy Kuen-Yew, Bryan Hooi Xiong, Caiming Stengel-Eskin, Elias Zhang, Hongyang Yin, Hongzhi Zhang, Huan Yao, Huaxiu Yoon, Jaehong Zhang, Jieyu Shu, Kai Zhu, Kaijie Krishna, Ranjay Swayamdipta, Swabha Shi, Taiwei Shi, Weijia Li, Xiang Li, Yiwei Hao, Yuexing Jia, Zhihao Li, Zhize Chen, Xiuying Tu, Zhengzhong Hu, Xiyang Zhou, Tianyi Zhao, Jieyu Sun, Lichao Huang, Furong Sasson, Or Cohen Sattigeri, Prasanna Reuel, Anka Lamparth, Max Zhao, Yue Dziri, Nouha Su, Yu Sun, Huan Ji, Heng Xiao, Chaowei Bansal, Mohit Chawla, Nitesh V. Pei, Jian Gao, Jianfeng Backes, Michael Yu, Philip S. Gong, Neil Zhenqiang Chen, Pin-Yu Li, Bo Song, Dawn Zhang, Xiangliang Computers and Society Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions. This paper presents a comprehensive framework to address these challenges through three key contributions. First, we systematically review global AI governance laws and policies from governments and regulatory bodies, as well as industry practices and standards. Based on this analysis, we propose a set of guiding principles for GenFMs, developed through extensive multidisciplinary collaboration that integrates technical, ethical, legal, and societal perspectives. Second, we introduce TrustGen, the first dynamic benchmarking platform designed to evaluate trustworthiness across multiple dimensions and model types, including text-to-image, large language, and vision-language models. TrustGen leverages modular components--metadata curation, test case generation, and contextual variation--to enable adaptive and iterative assessments, overcoming the limitations of static evaluation methods. Using TrustGen, we reveal significant progress in trustworthiness while identifying persistent challenges. Finally, we provide an in-depth discussion of the challenges and future directions for trustworthy GenFMs, which reveals the complex, evolving nature of trustworthiness, highlighting the nuanced trade-offs between utility and trustworthiness, and consideration for various downstream applications, identifying persistent challenges and providing a strategic roadmap for future research. This work establishes a holistic framework for advancing trustworthiness in GenAI, paving the way for safer and more responsible integration of GenFMs into critical applications. To facilitate advancement in the community, we release the toolkit for dynamic evaluation. |
| title | On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2502.14296 |