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.

翻译：随着机器学习系统和大数据的影响与分布日益扩大，数据高质量的重要性与日俱增。欧盟委员会拟议的《人工智能法案》更是对数据质量提出了具有挑战性的法律要求，尤其面向安全攸关的机器学习系统的市场准入。本文提出了一种支持多维度数据质量保证过程的新方法，该方法能够实现对定量数据质量需求的验证。我们通过小型示例数据集阐述并解释了该方案的概念与优势，并以基于手写数字的知名MNIST数据集为例，演示了该方法的具体应用。