Ensuring Dataset Quality for Machine Learning Certification

The 10th IEEE International Workshop on Software Certification (WoSoCer 2020) In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing standards dealing with data and show that the s...

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Bibliographic Details
Main Authors Picard, Sylvaine, Chapdelaine, Camille, Cappi, Cyril, Gardes, Laurent, Jenn, Eric, Lefèvre, Baptiste, Soumarmon, Thomas
Format Journal Article
LanguageEnglish
Published 03.11.2020
Subjects
Computer Science - Learning
Statistics - Computation
Statistics - Machine Learning
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Summary:The 10th IEEE International Workshop on Software Certification (WoSoCer 2020) In this paper, we address the problem of dataset quality in the context of Machine Learning (ML)-based critical systems. We briefly analyse the applicability of some existing standards dealing with data and show that the specificities of the ML context are neither properly captured nor taken into ac-count. As a first answer to this concerning situation, we propose a dataset specification and verification process, and apply it on a signal recognition system from the railway domain. In addi-tion, we also give a list of recommendations for the collection and management of datasets. This work is one step towards the dataset engineering process that will be required for ML to be used on safety critical systems.
DOI:10.48550/arxiv.2011.01799