  With the increasing capabilities of machine learning systems and their
potential use in safety-critical systems, ensuring high-quality data is
becoming increasingly important. In this paper we present a novel approach for
the assurance of data quality. For this purpose, the mathematical basics are
first discussed and the approach is presented using multiple examples. This
results in the detection of data points with potentially harmful properties for
the use in safety-critical systems.
