How Provenance helps Quality Assurance Activities in AI/ML Systems

Abstract

Quality assurance is required for the wide use of artificial intelligence (AI) systems in industry and society, including mission-critical areas such as medical or disaster management domains. However, the quality evaluation methods of machine learning (ML) components, especially deep neural networks, have not yet been established. In addition, various metrics are applied by evaluators with different quality requirements and testing environments, from data collection to experimentation to deployment. In this paper, we propose a quality provenance model, AIQPROV, to record who evaluated quality, when from which viewpoint, and how the evaluation was used. The AIQPROV model focuses on human activities on how to apply this to the field of quality assurance, where human intervention is required. Moreover, we present an extension of the W3C PROV framework and conduct a database to store the provenance information of the quality assurance lifecycle with 11 use cases to validate our model.