Ensuring Dataset Quality for Machine Learning Certification
This work addresses dataset engineering for safety-critical ML systems, but it is incremental as it builds on existing standards and focuses on a specific domain.
The paper tackles the problem of dataset quality for machine learning in safety-critical systems by proposing a dataset specification and verification process, applied to a railway signal recognition system, and provides recommendations for dataset collection and management.
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.