STARdom: an architecture for trusted and secure human-centered manufacturing systems
This addresses the need for secure human-centered AI systems in manufacturing, particularly for Industry 5.0, though it appears incremental as it builds on existing frameworks.
The authors tackled the lack of a unified architecture for trusted and secure AI systems with human involvement in manufacturing by proposing STARdom, which integrates forecasting, explainable AI, user feedback, active learning, and simulated reality; they validated it on a real-world demand forecasting case study.
There is a lack of a single architecture specification that addresses the needs of trusted and secure Artificial Intelligence systems with humans in the loop, such as human-centered manufacturing systems at the core of the evolution towards Industry 5.0. To realize this, we propose an architecture that integrates forecasts, Explainable Artificial Intelligence, supports collecting users' feedback, and uses Active Learning and Simulated Reality to enhance forecasts and provide decision-making recommendations. The architecture security is addressed as a general concern. We align the proposed architecture with the Big Data Value Association Reference Architecture Model. We tailor it for the domain of demand forecasting and validate it on a real-world case study.