Personalized Model-Based Design of Human Centric AI enabled CPS for Long term usage

2026-01-08

arXiv.org

AI Summary

This work addresses the challenge that human-centered AI systems, when deployed over extended periods, often violate safety and sustainability requirements when encountering unforeseen edge cases. To overcome the limitations of conventional testing approaches—particularly in terms of resource constraints, computational demands, and complex human–machine interactions—the study proposes a novel verification and design framework that integrates model-driven engineering, personalized modeling, and AI safety analysis. By establishing a personalized, model-driven analytical mechanism, the approach effectively identifies and mitigates latent safety and sustainability risks emerging during long-term deployment. This significantly enhances the robustness, reliability, and trustworthiness of human-centered cyber-physical systems operating in real-world environments.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• human-centric AI
• long-term operation
• corner cases
• safety
• security

Innovation

• personalized model-based design
• human-centric AI
• long-term CPS
• safety and security analysis
• edge case mitigation