Scholarlink

<p>AI safety is an emerging field of critical importance for the secure adoption and deployment of AI systems. With the recent advancements in large language models (LLMs), the technological landscape surrounding the design, development, and deployment of AI systems has undergone significant change. The failure of AI systems at one organization, or AI risks undertaken by one organization, can propagate down the AI technology supply chain, affect the entire AI ecosystem, and potentially lead to collective failures and cause large-scale harm to society. In this paper, we propose a novel architectural framework for understanding and analyzing AI safety in the context of LLMs, defining its characteristics through three key perspectives: Trustworthy AI, Responsible AI, and Ecosystemic Safe AI. We provide a comprehensive review of current research and advancements in AI safety from these perspectives, identifying major challenges and outlining mitigation strategies. Additionally, we highlight potential future directions that warrant further exploration to advance AI safety research and, ultimately, strengthen public trust in digital transformation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI safety landscape for large language models: taxonomy, state-of-the-art, and future directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月16日

查看全文
  • Chen Chen,
  • Xueluan Gong,
  • Ziyao Liu,
  • Weifeng Jiang,
  • Si Qi Goh,
  • Kwok-Yan Lam

摘要

AI safety is an emerging field of critical importance for the secure adoption and deployment of AI systems. With the recent advancements in large language models (LLMs), the technological landscape surrounding the design, development, and deployment of AI systems has undergone significant change. The failure of AI systems at one organization, or AI risks undertaken by one organization, can propagate down the AI technology supply chain, affect the entire AI ecosystem, and potentially lead to collective failures and cause large-scale harm to society. In this paper, we propose a novel architectural framework for understanding and analyzing AI safety in the context of LLMs, defining its characteristics through three key perspectives: Trustworthy AI, Responsible AI, and Ecosystemic Safe AI. We provide a comprehensive review of current research and advancements in AI safety from these perspectives, identifying major challenges and outlining mitigation strategies. Additionally, we highlight potential future directions that warrant further exploration to advance AI safety research and, ultimately, strengthen public trust in digital transformation.