用场景法应对无人机反制中的概率操纵，提升系统可信度。
Probability Hacking and the Design of Trustworthy ML for Signal Processing in C-UAS: A Scenario Based Method
- 通过场景分析识别机器学习在信号处理中的概率操纵风险
- 提出可嵌入现有法治机制的可信性要求
- 适合关注人机协同可信性的安防与军事系统设计者
为有效应对无人机系统（UAS）带来的各类威胁，需部署专用的反无人机系统（C-UAS）。将人工智能（AI）等新兴颠覆性技术（EDTs）引入C-UAS，可提升反制效能。本文采用场景化方法，研究基于机器学习（ML）的C-UAS在信号处理中的应用，将“概率操纵”识别为关键挑战，并提出可通过现有法治机制实现的可信性要求，以增强C-UAS的可信度，进而支撑民事与军事情境下的人机协同信任构建。关键词：C-UAS，场景法，新兴与颠覆性技术，概率操纵，可信性。
原文摘要 · Abstract (English)
In order to counter the various threats manifested by Unmanned Aircraft Systems (UAS) adequately, specialized Counter Unmanned Aircraft Systems (C-UAS) are required. Enhancing C-UAS with Emerging and Disruptive Technologies (EDTs) such as Artificial Intelligence (AI) can lead to more effective countermeasures. In this paper a scenario-based method is applied to C-UAS augmented with Machine Learning (ML), a subset of AI, that can enhance signal processing capabilities. Via the scenarios-based method we frame in this paper probability hacking as a challenge and identify requirements which can be implemented in existing Rule of Law mechanisms to prevent probability hacking. These requirements strengthen the trustworthiness of the C-UAS, which feed into justified trust - a key to successful Human-Autonomy Teaming, in civil and military contexts. Index Terms: C-UAS, Scenario-based method, Emerging and Disruptive Technologies, Probability hacking, Trustworthiness.
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