Contemporary artificial intelligence systems are pivotal in enhancing human efficiency and safety across various domains. One such domain is autonomous systems, especially in automotive and defense use cases. Artificial intelligence brings learning and enhanced decision-making to autonomy system goal-oriented behaviors and human independence. However, the lack of clear understanding of autonomy system capabilities hampers human-machine or machine-machine interaction and interdiction. This necessitates varying degrees of human involvement for safety, accountability, and explainability purposes. Yet, measuring the level autonomous capability in an autonomous system presents a challenge. Two scales of measurement exist, yet measuring autonomy presupposes a variety of elements not available in the wild. This is why existing measures for level of autonomy are operationalized only during design or test and evaluation phases. No measure for level of autonomy based on observed system behavior exists at this time. To address this, we outline a potential measure for predicting level of autonomy using observable actions. We also present an algorithm incorporating the proposed measure. The measure and algorithm have significance to researchers and practitioners interested in a method to blind compare autonomous systems at runtime. Defense-based implementations are likewise possible because counter-autonomy depends on robust identification of autonomous systems.

翻译：当代人工智能系统在提升人类效率与安全性方面发挥着关键作用，其应用领域涵盖自动驾驶与国防等自主系统。人工智能通过学习和增强决策能力，赋予自主系统目标导向行为与人类独立性。然而，由于对自主系统能力缺乏清晰认知，人机交互与机机交互及协同面临障碍。为确保安全性、可问责性与可解释性，需要不同程度的人类介入。但如何量化自主系统的自主能力水平仍存在挑战。现有两种度量尺度，但自主性评估通常预设多种在真实场景中难以获取的要素，导致现有自主性水平度量方法仅能在设计或测试评估阶段实施。目前尚未出现基于系统实际观测行为的自主性水平度量方法。为此，本文提出一种利用可观测行为预测自主性水平的潜在度量框架，并设计了融合该度量的算法。该度量方法与算法对需要在运行时对自主系统进行盲比较的研究者和实践者具有重要意义。基于国防的应用同样可行，因为反自主系统技术依赖于对自主系统的精准识别。