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

Fenareti Lampathaki, Carlos Agostinho, Yuri Glikman, Michele Sesana

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665449632
DOIs
Publication statusPublished - 21 Jun 2021
Event2021 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2021 - Cardiff, United Kingdom
Duration: 21 Jun 202123 Jun 2021

Conference

Conference2021 IEEE International Conference on Engineering, Technology and Innovation, ICE/ITMC 2021
Country/TerritoryUnited Kingdom
CityCardiff
Period21/06/2123/06/21

Keywords

  • Artificial Intelligence
  • Data Engineering
  • Explainable AI
  • Manufacturing

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