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Moving from 'black box' to 'glass box' artificial intelligence in manufacturing with XMANAI

 
: Lampathaki, Fenareti; Agostinho, Carlos; Glikman, Yury; Sesana, Michele

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Technology and Engineering Management Society -TEMS-:
IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2021 : 21 - 23 June 2021, Online
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-6654-4964-9
ISBN: 978-1-6654-4963-2
S.359-364
International Conference on Engineering, Technology and Innovation (ICE) <27, 2021, Online>
International Technology Management Conference (ITMC) <27, 2021, Online>
European Commission EC
H2020; 957362; XMANAI
Explainable Manufacturing Artificial Intelligence
Englisch
Konferenzbeitrag
Fraunhofer FOKUS ()
artificial intelligence; Explainable AI; Data Engineering; manufacturing

Abstract
Artificial Intelligence (AI) is finding its way into a broad range of industries, including manufacturing. The decisions and predictions that can be potentially derived from AI-enabled systems are becoming much more profound, and in many cases, critical to success and profitability. However, despite the indisputable benefits that AI can bring in society and in any industrial activity, humans typically have little insight about AI itself and even less concerning the knowledge on how AI systems make any decisions or predictions due to the so-called "black-box" effect. This paper presents the XMANAI approach, that focuses on explainable AI models and processes, to mitigate such an effect and reinforce trust. The aim is to transform the manufacturing value chain with ‘glass box’ models that are explainable to a ‘human in the loop’ and produce value-based explanations for data scientists, data engineers and business experts.

: http://publica.fraunhofer.de/dokumente/N-643618.html