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Non-binding diplomatic coordination, soft law agreements, and intergovernmental cooperation without treaty-level enforcement.
Also in Voluntary & Cooperative
As AI is a global technology, international cooperation is essential to creating effective and consistent regulatory frameworks. Countries should work together to develop shared standards and principles that can guide the responsible development and deployment of AI. Establishing global norms will help prevent regulatory arbitrage, where companies seek the least restrictive environments, and ensure that AI systems adhere to ethical standards no matter where they are developed or used. International organizations, such as the United Nations, and the Organization for Economic Co-operation and Development (OECD), are already taking steps to foster global dialogue on AI regulation. Future policy directions should build on these efforts, encouraging the creation of international treaties or agreements that promote ethical AI while balancing innovation with accountability. AI regulation is a rapidly evolving field that requires adaptable, forward-looking policies. A risk-based regulatory approach, ethical guidelines, and international cooperation are key to ensuring that AI technologies contribute positively to society while mitigating their potential risks. Future policymakers must work collaboratively with stakeholders across sectors and borders to create a regulatory environment that fosters innovation, protects human rights, and ensures that AI serves the common good.
Reasoning
Non-binding diplomatic coordination harmonizing AI standards across nations without state enforcement.
Policies
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 failure