STARdom: an architecture for trusted and secure human-centered manufacturing systems
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 2 Apr 2021.
Builds on
8 of 21 references.
References · 8
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2016Concrete Problems in AI Safetycited by 1,642
- 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 365
- 2018Adversarial Examples: Opportunities and Challengescited by 63
- 2019When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signaturescited by 40
- 2019Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analyticscited by 24
- 2021Directive Explanations for Actionable Explainability in Machine Learning Applicationscited by 14
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Citing papers per year: 2022: 1.
1 paper.