Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications
Not on the map. Why?
Abstract (arXiv)
Human-centricity is the core value behind the evolution of manufacturing towards Industry 5.0. Nevertheless, there is a lack of architecture that considers safety, trustworthiness, and human-centricity at its core. Therefore, we propose an architecture that integrates Artificial Intelligence (Active Learning, Forecasting, Explainable Artificial Intelligence), simulated reality, decision-making, and users' feedback, focusing on synergies between humans and machines. Furthermore, we align the proposed architecture with the Big Data Value Association Reference Architecture Model. Finally, we validate it on three use cases from real-world case studies.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 43 of its 195 references that are in the map. First posted on arXiv 21 Mar 2022.
Builds on
43 of 195 references.
References · 43
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,733
- 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
- 2017Axiomatic Attribution for Deep Networkscited by 3,384
- 2016Concrete Problems in AI Safetycited by 1,642
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
- 2017Learning Important Features Through Propagating Activation Differencescited by 1,458
- 2019Modeling Tabular data using Conditional GANcited by 699
- 2017Generative Adversarial Networks: An Overviewcited by 590
- 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
- 2019Federated Machine Learning: Concept and Applicationscited by 412
31 more
- 2016European Union regulations on algorithmic decision-making and a "right to explanation"cited by 344
- 2019Variational Adversarial Active Learningcited by 284
- 2018Residual Reinforcement Learning for Robot Controlcited by 274
- 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 254
- 2019A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysiscited by 151
- 2019Towards Explainable Artificial Intelligencecited by 106
- 2021If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniquescited by 82
- 2018Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Networkcited by 74
- 2017What Does Explainable AI Really Mean? A New Conceptualization of Perspectivescited by 72
- 2017Generative Adversarial Active Learningcited by 70
- 2019Understanding artificial intelligence ethics and safetycited by 68
- 2018Adversarial Sampling for Active Learningcited by 51
- 2020A Survey of Active Learning for Text Classification using Deep Neural Networkscited by 50
- 2018Pool-Based Sequential Active Learning for Regressioncited by 33
- 2021An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainabilitycited by 16
- 2020Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselagecited by 14
- 2021Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognitioncited by 10
- 2018Open the Black Box Data-Driven Explanation of Black Box Decision Systemscited by 9
- 2018Cyber Threat Intelligence : Challenges and Opportunitiescited by 5
- 2021XAI-KG: knowledge graph to support XAI and decision-making in manufacturingcited by 5
- 2021Artificial Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challengescited by 5
- 2019Defective samples simulation through Neural Style Transfer for automatic surface defect segmentcited by 4
- 2021Streaming Machine Learning and Online Active Learning for Automated Visual Inspectioncited by 4
- 2019Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent Manufacturingcited by 3
- 2020A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costscited by 3
- 2020A Survey of Cybersecurity of Digital Manufacturingcited by 3
- 2021Reframing demand forecasting: a two-fold approach for lumpy and intermittent demandcited by 3
- 2021Active Learning for Automated Visual Inspection of Manufactured Productscited by 3
- 2021Jasmine: A New Active Learning Approach to Combat Cybercrimecited by 2
- 2018Optimal Sabotage Attack on Composite Material Partscited by 1
- 2021STARdom: an architecture for trusted and secure human-centered manufacturing systemscited by 1
Built on by
Citing papers per year: 2022: 3; 2023: 2; 2024: 0; 2025: 1; 2026: 5.
11 papers.
- 2023Worker Activity Recognition in Manufacturing Line Using Near-body Electric Fieldcited by 6
- 2026Position: Human-Centric AI Requires a Minimum Viable Level of Human Understandingcited by 6
- 2022Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturingcited by 4
- 2025Bridging Industrial Expertise and XR with LLM-Powered Conversational Agentscited by 4
- 2023XAI for time-series classification leveraging image highlight methodscited by 2
- 2022Enriching Artificial Intelligence Explanations with Knowledge Fragmentscited by 1
- 2022Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniquescited by 1
- 2026Native Explainability for Bayesian Confidence Propagation Neural Networks: A Framework for Trusted Brain-Like AIcited by 0
- 2026Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directionscited by 0
- 2026EdgeHAR: An Edge-Native Compact Sensor Foundation Model for Human Activity Recognitioncited by 0
- 2026A Comprehensive Review of Generative Physical Artificial Intelligencecited by 0
- 2026A Comprehensive Review of Generative Physical Artificial Intelligence16 Sep 2026
- 2026EdgeHAR: An Edge-Native Compact Sensor Foundation Model for Human Activity Recognition13 Sep 2026
- 2026Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions27 Jul 2026
- 2026Native Explainability for Bayesian Confidence Propagation Neural Networks: A Framework for Trusted Brain-Like AI12 May 2026
- 2026Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding31 Jan 2026
- 2025Bridging Industrial Expertise and XR with LLM-Powered Conversational Agents7 Apr 2025
- 2023XAI for time-series classification leveraging image highlight methods28 Nov 2023
- 2023Worker Activity Recognition in Manufacturing Line Using Near-body Electric Field7 Aug 2023
- 2022Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniques19 Dec 2022
- 2022Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturing12 Sep 2022
- 2022Enriching Artificial Intelligence Explanations with Knowledge Fragments12 Apr 2022