Reference : Challenges Towards Production-Ready Explainable Machine Learning
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/44318
Challenges Towards Production-Ready Explainable Machine Learning
English
Veiber, Lisa mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Allix, Kevin mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
Arslan, Yusuf mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Bissyande, Tegawendé François D Assise mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Klein, Jacques mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
Jul-2020
Proceedings of the 2020 USENIX Conference on Operational Machine Learning (OpML 20)
Veiber, Lisa mailto
Allix, Kevin mailto
Arslan, Yusuf mailto
Bissyande, Tegawendé François D Assise mailto
Klein, Jacques mailto
USENIX Association
Yes
International
978-1-939133-15-1
2020 USENIX Conference on Operational Machine Learning
28-07-2020 to 07-08-2020
USENIX
CA
[en] machine learning ; explanations
[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.
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/44318
https://www.usenix.org/conference/opml20/presentation/veiber

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