Paper

White Paper Machine Learning in Certified Systems

Delseny, Hervé, Chapdelaine, Camille, Beltran, Brice, Flandin, Gregory, Lefevre, Baptiste, Gabreau, Christophe, Jenn, Eric, Bonnin, Hugues, Pasquet, Sylvain, Gauffriau, Adrien, Gardès, Laurent, Pagetti, Claire, Picard, Sylvaine, De Grancey, Florence, Gerchinovitz, Sébastien, Cappi, Cyril, Albore, Alexandre, Beaudouin, Bernard, Martinez, Ghilaine, Delmas, Kevin, Jean-Brice Ginestet, Duchel, Didier, Mamalet, Franck, Alecu, Lucian, Gabriel, Jean-Marc, Ponsolle, Ludovic, Hervieu, Alexandre, Damour, Mathieu

2021

Overview

Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exupéry de Toulouse (IRT), as part of the DEEL Project.

• Publisher: Cornell University Library, arXiv.org
• Subject: Certification / Machine learning / Paper machines / Task complexity / Voice recognition