揭示分布偏移与AI安全的深层关联，推动两领域方法融合。
Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies
- 识别两类分布偏移与安全问题的对应关系
- 部分偏移与安全问题可相互形式化转换
- 为两领域协同研究提供统一框架
本文通过系统分析分布偏移与AI安全在概念和方法上的协同关系，弥合两者间的鸿沟。以往研究多局限于特定案例或非正式类比，本文提出两种具体关联：(1) 某类分布偏移的应对方法有助于实现相应的安全目标；(2) 某些分布偏移与安全问题可被形式化地相互约化，从而实现方法互用。研究结果为两领域整合提供了统一视角，推动更深层次的交叉融合。
原文摘要 · Abstract (English)
This paper bridges distribution shift and AI safety through a comprehensive analysis of their conceptual and methodological synergies. While prior discussions often focus on narrow cases or informal analogies, we establish two types connections between specific causes of distribution shift and fine-grained AI safety issues: (1) methods addressing a specific shift type can help achieve corresponding safety goals, or (2) certain shifts and safety issues can be formally reduced to each other, enabling mutual adaptation of their methods. Our findings provide a unified perspective that encourages deeper integration between distribution shift and AI safety research.
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