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2023
Conference Paper
Title

Industrial Asset Management and Secure Sharing for an XAI Manufacturing Platform

Abstract
Explainable AI is an emerging field that aims to address how black-box decisions of AI systems are made, by attempting to understand the steps and models involved in this decision-making. Explainable AI in manufacturing is supposed to deliver predictability, agility, and resiliency across targeted manufacturing apps. In this context, large amounts of data, which can be of high sensitivity and various formats need to be securely and efficiently handled. This paper proposes an Asset Management and Secure Sharing solution tailored to the Explainable AI and Manufacturing context in order to tackle this challenge. The proposed asset management architecture enables an extensive data management and secure sharing solution for industrial data assets. Industrial data can be pulled, imported, managed, shared, and tracked with a high level of security using this design. This paper describes the solution´s overall architectural design and gives an overview of the functionalities and incorporated technologies of the involved components, which are responsible for data collection, management, provenance, and sharing as well as for overall security.
Author(s)
Reji, Sangeetha
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Hetterich, Jonas
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Pitsios, Stamatis
Gkolemi, Vasilis
Perez-Castanos, Sergi
Pertselakis, Minas
Mainwork
Proceedings of the 29th International Conference on Engineering, Technology, and Innovation, ICE 2023  
Conference
International Conference on Engineering, Technology and Innovation 2023  
DOI
10.1109/ICE/ITMC58018.2023.10332309
Language
English
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS  
Keyword(s)
  • Explainable Artificial Intelligence(

  • REST API

  • Data Provenance

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