Human centric critical systems are increasingly involving artificial intelligence to enable knowledge extraction from sensor collected data. Examples include medical monitoring and control systems, gesture based human computer interaction systems, and autonomous cars. Such systems are intended to operate for a long term potentially for a lifetime in many scenarios such as closed loop blood glucose control for Type 1 diabetics, self-driving cars, and monitoting systems for stroke diagnosis, and rehabilitation. Long term operation of such AI enabled human centric applications can expose them to corner cases for which their operation is may be uncertain. This can be due to many reasons such as inherent flaws in the design, limited resources for testing, inherent computational limitations of the testing methodology, or unknown use cases resulting from human interaction with the system. Such untested corner cases or cases for which the system performance is uncertain can lead to violations in the safety, sustainability, and security requirements of the system. In this paper, we analyze the existing techniques for safety, sustainability, and security analysis of an AI enabled human centric control system and discuss their limitations for testing the system for long term use in practice. We then propose personalized model based solutions for potentially eliminating such limitations.

翻译：人本关键系统日益融入人工智能技术，以实现从传感器采集数据中提取知识。典型应用包括医疗监护控制系统、基于手势的人机交互系统以及自动驾驶汽车。此类系统通常设计为长期运行，在某些场景下甚至需要终身运作，例如1型糖尿病患者的闭环血糖控制系统、自动驾驶汽车，以及用于中风诊断与康复的监测系统。这类AI赋能的人本应用在长期运行过程中可能遭遇极端场景，导致其运行状态存在不确定性。这种不确定性可能源于多种因素：设计中的固有缺陷、测试资源受限、测试方法本身的计算局限性，或人机交互产生的未知使用场景。未经测试的极端场景或系统性能不确定的场景，可能导致系统在安全性、可持续性与安全性要求方面出现违规。本文系统分析了现有AI赋能在人本控制系统安全性、可持续性与安全性分析方面的技术，并探讨了这些技术在长期实际应用测试中的局限性。在此基础上，我们提出了基于个性化模型的解决方案，以期从根本上消除这些局限。