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.
@article{arxiv.2104.00983,
title = {STARdom: an architecture for trusted and secure human-centered manufacturing systems},
author = {Jože M. Rožanec and Patrik Zajec and Klemen Kenda and Inna Novalija and Blaž Fortuna and Dunja Mladenić and Entso Veliou and Dimitrios Papamartzivanos and Thanassis Giannetsos and Sofia Anna Menesidou and Rubén Alonso and Nino Cauli and Diego Reforgiato Recupero and Dimosthenis Kyriazis and Georgios Sofianidis and Spyros Theodoropoulos and John Soldatos},
journal= {arXiv preprint arXiv:2104.00983},
year = {2021}
}