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Challenges Towards Production-Ready Explainable Machine Learning
Veiber, Lisa; Allix, Kevin; Arslan, Yusuf et al.
2020In Veiber, Lisa; Allix, Kevin; Arslan, Yusuf et al. (Eds.) Proceedings of the 2020 USENIX Conference on Operational Machine Learning (OpML 20)
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Keywords :
machine learning; explanations
Abstract :
[en] Machine Learning (ML) is increasingly prominent in or- ganizations. While those algorithms can provide near perfect accuracy, their decision-making process remains opaque. In a context of accelerating regulation in Artificial Intelligence (AI) and deepening user awareness, explainability has become a priority notably in critical healthcare and financial environ- ments. The various frameworks developed often overlook their integration into operational applications as discovered with our industrial partner. In this paper, explainability in ML and its relevance to our industrial partner is presented. We then dis- cuss the main challenges to the integration of ex- plainability frameworks in production we have faced. Finally, we provide recommendations given those challenges.
Disciplines :
Computer science
Author, co-author :
Veiber, Lisa ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Allix, Kevin ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC)
Arslan, Yusuf ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Bissyande, Tegawendé François D Assise  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Klein, Jacques ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC)
External co-authors :
no
Language :
English
Title :
Challenges Towards Production-Ready Explainable Machine Learning
Publication date :
July 2020
Event name :
2020 USENIX Conference on Operational Machine Learning
Event organizer :
USENIX
Event place :
United States - California
Event date :
28-07-2020 to 07-08-2020
Audience :
International
Main work title :
Proceedings of the 2020 USENIX Conference on Operational Machine Learning (OpML 20)
Publisher :
USENIX Association
ISBN/EAN :
978-1-939133-15-1
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
Available on ORBilu :
since 21 September 2020

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