Towards Certified Safe Personalization in Learning-Enabled Human-in-the-loop Human-in-the-plant Systems

This article presents an AI-enabled Personalization Management (AIIM) software for human-in-the-loop, human-in-the-plant learning-enabled systems (LES). AIIM can be integrated with LES software to aid a human user in achieving safe and effective operation under dynamically changing contexts. AIIM consists of: (a) an AI technique to derive model coefficient of a physics-guided surrogate model from operational data shared following privacy norms, and (b) continuous model conformance to identify key changes in LES operational behavior that may jeopardize safety. We demonstrate two capabilities of AIIM, personalization and unknown error detection, through case studies that span a significant breadth of dynamic context change scenarios including (a) involuntary change in user context such as medication-induced glucose metabolism change in automated insulin delivery (AID), (b) actuation failure such as cartridge blockage in AID, (c) latent sensor error in aviation, and (d) unknown coding error in autonomous car software patches. We compare AIIM personalization with human-in-the-loop and self-adaptive model-predictive control design in real-life and simulation settings, to show safe and improved diabetes management.