ABSTRACT. Machine learning systems typically involve complex decision making mechanisms while lack clear and concise specifications. Demonstrating the quality of machine learning systems therefore is a challenging task. We propose an approach combining formal methods and metamorphic testing for improving the quality of machine learning systems. In particular, our framework enables the possibility of developing policing functions for runtime monitoring machine learning systems based on metamorphic relations.