跨越国界的怀疑：算法域外管辖与人工智能驱动的金融监控
Becoming Suspicious Across Borders: Algorithmic Extraterritoriality and AI-Driven Financial Surveillance
浏览论文内容
中文总结 AI 辅助
本文提出算法域外管辖概念，指出AI驱动的金融监控通过跨国数据基础设施使怀疑产生于数据过程，挑战了传统司法管辖与问责机制。
中文摘要 AI 辅助
在反洗钱与反恐怖融资（AML/CFT）领域，怀疑是一个重要但难以捉摸的概念，它允许在证据门槛以下进行干预。在其传统形式中，怀疑可以被理解为在可识别的司法管辖区内，由人类行为者作出的情境化法律判断。本文认为，这种理解已不再充分。随着人工智能（AI）成为金融监控的组成部分，怀疑越来越多地通过数据驱动过程产生。这种转变既是认识论上的，也是空间上的。由于AI驱动的金融监控通过跨国数据基础设施运作，监管触达范围与其说是行为发生地的问题，不如说是此类行为是否在数据系统中变得可见的问题。本文提出了算法域外管辖的概念，将其理解为一种由数据基础设施而非正式管辖权主张所中介的监管权力形式。此外，由于个体越来越多地被构建为数据化的怀疑对象，他们通过分散且不透明的评估过程变得可被治理。这对问责性和可争议性构成了挑战，因为怀疑变得更加难以定位、解释或质疑。
英文摘要
Suspicion is an important, yet elusive concept in anti-money laundering and counter-terrorist financing (AML/CFT), which allows for intervention below the threshold of proof. In its traditional form, suspicion can be understood as a situated legal judgement by human actors within identifiable jurisdictions. It is argued that this understanding is no longer adequate. As artificial intelligence (AI) becomes an integral part of financial surveillance, suspicion is increasingly produced through data-driven processes. This transformation is epistemic, but also spatial. Since AI-driven financial surveillance operates through transnational data infrastructures, regulatory reach is less a matter of where conduct occurs than a question of whether such conduct becomes visible within data systems. This article develops the concept of algorithmic extraterritoriality, understood as a form of regulatory power mediated by data infrastructures rather than formal assertions of jurisdiction. Moreover, since individuals are increasingly constituted as datafied subjects of suspicion, they are rendered governable through dispersed and opaque processes of evaluation. This constitutes a challenge for accountability and contestability because suspicion becomes more difficult to locate, explain or contest.
发表机构
- Neapolis University Pafos（帕福斯尼亚波利斯大学）
机构由 AI 辅助整理，请以论文原文为准。
