Ensuring Dataset Quality for Machine Learning Certification

Sylvaine Picard (1) , Camille Chapdelaine (1) , Cyril Cappi (2) , Laurent Gardes (2) , Eric Jenn , Baptiste Lefevre , Thomas Soumarmon

1 SAFRAN Group
2 SNCF

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 account. 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 addition, 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.

Mots clés: datasets; certification process; machine learning; datasets

Domaines: Intelligence artificielle [cs.AI]

Sylvaine Picard, Camille Chapdelaine, Cyril Cappi, Laurent Gardes, Eric Jenn, et al.. Ensuring Dataset Quality for Machine Learning Certification. 2021. ⟨hal-03355371⟩