4 research outputs found
Explainable Artificial Intelligence to Enhance Data Trustworthiness in Crowd-Sensing Systems
No full textAround the world there has been an advancement of IoT edge devices, that in turn have enabled the collection of rich datasets as part of the Mobile Crowd Sensing (MCS) paradigm, which in practice is implemented in a variety of safety critical applications. In spite of the advantages of such datasets, there exists an inherent data trustworthiness challenge due to the interference of malevolent actors. In this context, there has been a great body of proposed solutions which capitalize on conventional machine algorithms for sifting through faulty data without any assumptions on the trustworthiness of the source. However, there is still a number of open issues, such as how to cope with strong colluding adversaries, while in parallel managing efficiently the sizable influx of user data. In this work we suggest that the usage of explainable artificial intelligence (XAI) can lead to even more efficient performance as we tackle the limitation of conventional black box models, by enabling the understanding and interpretation of a model's operation. Our approach enables the reasoning of the model's accuracy in the presence of adversaries and has the ability to shun out faulty or malicious data, thus, enhancing the model's adaptation process. To this end, we provide a prototype implementation coupled with a detailed performance evaluation under different scenarios of attacks, employing both real and synthetic datasets. Our results suggest that the use of XAI leads to improved performance compared to other existing schemes
Human in the AI loop via xAI and Active Learning for Visual Inspection
No full textIndustrial revolutions have historically disrupted manufacturing by introducing automation into production. Increasing automation reshapes the role of the human worker. Advances in robotics and artificial intelligence open new frontiers of human-machine collaboration. Such collaboration can be realized considering two sub-fields of artificial intelligence: active learning and explainable artificial intelligence. Active learning aims to devise strategies that help obtain data that allows machine learning algorithms to learn better. On the other hand, explainable artificial intelligence aims to make the machine learning models intelligible to the human person. The present work first describes Industry 5.0, human-machine collaboration, and state-of-the-art regarding quality inspection, emphasizing visual inspection. Then it outlines how human-machine collaboration could be realized and enhanced in visual inspection. Finally, some of the results obtained in the EU H2020 STAR project regarding visual inspection are shared, considering artificial intelligence, human digital twins, and cybersecurity
SECONDO: A platform for cybersecurity investments and cyber insurance decisions
No full textThis paper represents the SECONDO framework to assist organizations with decisions related to cybersecurity investments and cyber-insurance. The platform supports cybersecurity and cyber-insurance decisions by implementing and integrating a number of software components. SECONDO operates in three distinct phases: (i) cyber-physical risk assessment and continuous monitoring; (ii) investment-driven optimized cyber-physical risk control; and (iii) blockchain-enabled cyber-insurance contract preparation and maintenance. Insurers can leverage SECONDO functionalities to actively participate in the management of cyber-physical risks of a shipping company to reduce their insured risk
STARdom: An Architecture for Trusted and Secure Human-Centered Manufacturing Systems
No full textPart 5: Digital Twins Based on Systems Engineering and Semantic ModelingInternational audienceThere 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 at all levels. 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
