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arXiv cs.AI· Maud van Lier, Ludovica Marinucci, Vieri Giuliano Santucci, Raja Chatila, St\'ephane Doncieux, \'Emiland Garrab\'e, Richard Duro·· 3 天前AI 评分22

可接受的失准:自主机器人价值对齐为何无法完全实现

Acceptable Misalignment

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研究者提出"可接受的失准"概念,认为自主机器人的价值对齐问题在理论和实践上都无法完全实现,失准事件将持续发生。论文主张从追求对齐最大化转向关注失准事件变得可接受的条件,并给出对齐机制的规范性要求及一套新的方法论框架。

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Abstract:There is a specific challenge designers face in developing autonomous robots capable of open-ended learning. This challenge is how one can develop such systems in an ethically sound way when there are many unknowns during design time about their manner of deployment, what they can and will learn, and how these systems will act on the basis of what they have learned. One response to this challenge is to align, as far as possible, the activities and goals of autonomous robots with human values, raising the question of how to make this alignment possible, a question known as the value alignment problem. We argue that the value alignment problem faces a number of challenges that show that full alignment is both theoretically and practically impossible, meaning that misalignment incidents will keep occurring. This undermines current alignment approaches, as the attempt to maximize alignment cannot properly deal with the possibility that the risk of misalignment that remains after alignment methods have been implemented might still be unacceptable to society. To overcome this difficulty, we argue that a shift in perspective is required from a focus on the maximization of alignment to the conditions under which misalignment incidents become acceptable. In this paper, we explore what this shift in perspective entails. We introduce the concept of acceptable misalignment, formulate normative requirements for alignment mechanisms aimed at bringing misalignment incidents to an acceptable level, and we conclude by presenting a new methodological framework that consists of alignment mechanisms that can meet these requirements.
Comments: 16 pages, 1 figure
Subjects: Artificial Intelligence (cs.AI); Robotics (cs.RO)
Cite as: arXiv:2610.16881 [cs.AI]
  (or arXiv:2610.16881v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2610.16881

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Maud Van Lier [view email]
[v1] Mon, 12 Oct 2026 14:42:46 UTC (571 KB)

来源:arXiv cs.AI · arxiv.org