Quality Assurance for Machine Learning – an approach to function and system safeguarding

Abstract

In an industrial context, high software quality is mandatory in order to avoid costly patching. We present a state of the art analysis of approaches to ensure that a specific Artificial Intelligence (AI) model is ready for release. We analyze the requirements a Machine Learning (ML) system has to fulfill in order to comply with the needs of an automotive OEM. The main implication for projects relying on ML is a holistic assessment of possible quality risks. These risks may stem from implemented ML models and spread into the delivery. We present a methodological quality assurance (QA) approach and its evaluation.