数字应声虫:如何应对谄媚的军事人工智能?

IF 2.2 3区 社会学 Q1 INTERNATIONAL RELATIONS
Jonathan Kwik
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引用次数: 0

摘要

军方越来越多地采用决策支持人工智能来定位和其他规划任务。与这些模型相关的一个新兴风险是“谄媚”:人工智能倾向于将其输出与用户的观点或偏好保持一致,即使这种观点是不正确的。本文对军事领域的谄媚人工智能提供了一个初步的视角,并确定了不同的技术、组织和操作要素,为更细致的研究提供信息。它从技术上考察了这一现象,它给军事行动带来的风险,以及军队可以采取的不同行动方案来减轻这种风险。该理论认为,从短期和长期来看,阿谀奉承在军事上都是有害的,因为它们分别加剧了现有的认知偏见,并引发了组织的过度信任。然后,本文探讨了可以采取的两种主要缓解方法:模型/设计级别的技术干预(例如,通过微调)和用户培训。从理论上讲,用户培训是技术干预的重要补充措施,因为奉承永远不可能只在设计阶段全面解决。最后,本文概念化了军队可以开发的工具和程序,以尽量减少阿谀奉承的人工智能可能对用户决策产生的负面影响,如果阿谀奉承的人工智能出现,尽管之前已经做出了所有缓解努力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digital Yes-Men: How to Deal With Sycophantic Military AI?

Militaries have increasingly embraced decision-support AI for targeting and other planning tasks. An emerging risk identified with respect to these models is ‘sycophancy’: the tendency of AI to align their outputs with their user's views or preferences, even if this view is incorrect. This paper offers an initial perspective on sycophantic AI in the military domain, and identifies the different technical, organisational and operational elements at play to inform more granular research. It examines the phenomenon technically, the risks it introduces to military operations, and the different courses-of-action militaries can take to mitigate this risk. It theorises that sycophancy is militarily deleterious both in the short and long term, by aggravating existing cognitive biases and inducing organisational overtrust, respectively. The paper then explores two main approaches to mitigation that can be taken: technical intervention at the model/design level (e.g., through finetuning), and user training. It theorises that user training is an important complementary measure to technical intervention, since sycophancy can never be comprehensively addressed only at the design stage. Finally, the paper conceptualises tools and procedures militaries could develop to minimise the negative effects sycophantic AI could have on users' decision-making should sycophancy manifest despite all prior efforts at mitigation.

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来源期刊
Global Policy
Global Policy Multiple-
CiteScore
3.60
自引率
10.50%
发文量
125
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