The importance of high data quality is increasing with the growing impact and
distribution of ML systems and big data. Also the planned AI Act from the
European commission defines challenging legal requirements for data quality
especially for the market introduction of safety relevant ML systems. In this
paper we introduce a novel approach that supports the data quality assurance
process of multiple data quality aspects. This approach enables the
verification of quantitative data quality requirements. The concept and
benefits are introduced and explained on small example data sets. How the
method is applied is demonstrated on the well known MNIST data set based an
handwritten digits.

高数据质量的重要性随着机器学习系统和大数据的影响和分布而增加。欧洲委员会提出的计划的 AI 法案特别针对与安全相关的机器学习系统的市场引入，定义了具有挑战性的数据质量法律要求。本文介绍了一种新的方法，支持多种数据质量方面的数据质量保证过程。该方法可以验证定量数据质量要求。通过小的示例数据集介绍和解释了该概念和好处。通过对著名的 MNIST 数据集进行基于手写数字的应用演示了该方法的应用。