Probability Hacking and the Design of Trustworthy ML for Signal Processing in C-UAS: A Scenario Based Method

TL;DR

This paper introduces a scenario-based approach to enhance the trustworthiness of AI-augmented C-UAS systems by addressing probability hacking threats, ensuring reliable signal processing for civil and military applications.

Contribution

It presents a novel scenario-based method to identify requirements that prevent probability hacking, thereby improving trustworthiness of ML in C-UAS.

Findings

Identified key requirements to prevent probability hacking.

Enhanced trustworthiness of ML in C-UAS systems.

Framework applicable to civil and military contexts.

Abstract

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.