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Non-binding diplomatic coordination, soft law agreements, and intergovernmental cooperation without treaty-level enforcement.
Also in Voluntary & Cooperative
AI safety governance must evolve to address the growing complexity and global integration of AI technologies. Future directions emphasize the need for multilateral regulatory frameworks that harmonize standards across jurisdictions, ensuring interoperability and joint enforcement mechanisms. Such frameworks should account for diverse ethical and cultural values, integrating cross-sector collaborations that bring together technical, legal, and ethical expertise. Interdisciplinary approaches are essential, particularly in developing governance tools that incorporate both technical and ethical metrics, and conducting human-AI interaction studies to ensure AI systems function equitably across different socio-economic contexts. Moreover, AI governance must focus on multivalent value systems, where ethical imperatives—such as inclusivity and sustainability—shape regulatory practices.
A key priority is the establishment of global AI safety communities that foster continuous dialogue and collaboration. These communities should consist of transdisciplinary research consortia, participatory governance platforms, and educational initiatives, ensuring diverse voices contribute to shaping AI safety standards. Finally, AI safety governance must address geopolitical and ethical considerations, such as AI’s role in international relations and its impact on global inequalities, which require coordinated strategies to mitigate risks and ensure safe AI deployment globally. This holistic, inclusive approach will help AI governance frameworks evolve in tandem with the challenges posed by advanced AI systems.
Reasoning
International cooperation and interdisciplinary community building coordinate AI safety across organizations without state enforcement.
Future directions
Value Misalignment
99.9 OtherValue Misalignment > Mitigating social bias
1 AI SystemValue Misalignment > Privacy protection
1 AI SystemValue Misalignment > Methods for mitigating toxicity
1 AI SystemValue Misalignment > Methods for mitigating LLM amorality
1 AI SystemRobustness to attack
1 AI SystemLarge Language Model Safety: A Holistic Survey
Shi, Dan; Shen, Tianhao; Huang, Yufei; Li, Zhigen; Leng, Yongqi; Jin, Renren; Liu, Chuang; Wu, Xinwei; Guo, Zishan; Yu, Linhao; Shi, Ling; Jiang, Bojian; Xiong, Deyi (2024)
The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural language understanding and generation. However, the increasing integration of these models into critical applications raises substantial safety concerns, necessitating a thorough examination of their potential risks and associated mitigation strategies. This survey provides a comprehensive overview of the current landscape of LLM safety, covering four major categories: value misalignment, robustness to adversarial attacks, misuse, and autonomous AI risks. In addition to the comprehensive review of the mitigation methodologies and evaluation resources on these four aspects, we further explore four topics related to LLM safety: the safety implications of LLM agents, the role of interpretability in enhancing LLM safety, the technology roadmaps proposed and abided by a list of AI companies and institutes for LLM safety, and AI governance aimed at LLM safety with discussions on international cooperation, policy proposals, and prospective regulatory directions. Our findings underscore the necessity for a proactive, multifaceted approach to LLM safety, emphasizing the integration of technical solutions, ethical considerations, and robust governance frameworks. This survey is intended to serve as a foundational resource for academy researchers, industry practitioners, and policymakers, offering insights into the challenges and opportunities associated with the safe integration of LLMs into society. Ultimately, it seeks to contribute to the safe and beneficial development of LLMs, aligning with the overarching goal of harnessing AI for societal advancement and well-being. A curated list of related papers has been publicly available at https://github.com/tjunlp-lab/Awesome-LLM-Safety-Papers.
Other (outside lifecycle)
Outside the standard AI system lifecycle
Governance Actor
Regulator, standards body, or oversight entity shaping AI policy
Govern
Policies, processes, and accountability structures for AI risk management
Primary
6.5 Governance failureOther
