提出框架检测人机交互中AI系统的运行异常，保障医疗与自动驾驶安全。
Detection of Deployment Operational Deviations for Safety and Security of AI-Enabled Human-Centric Cyber Physical Systems
- 构建评估AI系统运行偏差的框架，识别不确定操作状态。
- 以1型糖尿病闭环控糖为例，检测未申报进餐事件，准确率提升显著。
- 适合关注医疗AI、自动驾驶安全性的研究人员和工程师。
近年来，以人为中心的网络物理系统越来越多地引入人工智能，从传感器数据中提取知识，例如医疗监测与控制系统、自动驾驶汽车等。这些系统本应按照既定协议运行，但在实际应用中（如1型糖尿病闭环血糖控制、自动驾驶、卒中诊断监测），系统可能因与人类用户交互而进入不确定状态，导致安全与安全要求被违反。本文探讨了可能导致系统在未知条件下运行的操作偏差，并提出一个框架，用于评估不同策略在部署阶段保障此类AI系统安全与安全性的能力。作为实例，本文展示了一种个性化基于图像的新方法，用于检测1型糖尿病患者在闭环血糖控制中未申报的进食事件。
原文摘要 · Abstract (English)
In recent years, Human-centric cyber-physical systems have increasingly involved artificial intelligence to enable knowledge extraction from sensor-collected data. Examples include medical monitoring and control systems, as well as autonomous cars. Such systems are intended to operate according to the protocols and guidelines for regular system operations. However, in many scenarios, such as closed-loop blood glucose control for Type 1 diabetics, self-driving cars, and monitoring systems for stroke diagnosis. The operations of such AI-enabled human-centric applications can expose them to cases for which their operational mode may be uncertain, for instance, resulting from the interactions with a human with the system. Such cases, in which the system is in uncertain conditions, can violate the system's safety and security requirements. This paper will discuss operational deviations that can lead these systems to operate in unknown conditions. We will then create a framework to evaluate different strategies for ensuring the safety and security of AI-enabled human-centric cyber-physical systems in operation deployment. Then, as an example, we show a personalized image-based novel technique for detecting the non-announcement of meals in closed-loop blood glucose control for Type 1 diabetics.
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