Conformity Assessment of Machine Learning-Based Quality Assurance: A Gap Analysis

Although machine learning (ML) has been widely utilized across industries, its integration into conformity assessment processes is still a key challenge. This paper proposes a framework that enables ML systems to replace manual and destructive testing by directly conducting conformity assessments based on process parameters. The framework aligns with standards like Verband der Automobilindustrie (VDA) Volume 5 to support the development of ML-based conformity assessment systems. To enable this integration, gaps in process integration, role definitions, and technical validation are identified through a gap analysis. The framework addresses process- and role-related gaps by adapting each step of the VDA Volume 5 development process—planning, validation, calibration, and deployment—to meet conformity requirements. Developed during the planning phase at a German automotive manufacturer, this framework provides a structured approach for designing ML systems that comply with industry standards, contributing to more efficient and reliable quality assurance (QA) processes.