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。

QI2 数据质量保证的交互式工具

QI2 -- an Interactive Tool for Data Quality Assurance

Statistical production systems cover multiple steps from the collection,
aggregation, and integration of data to tasks like data quality assurance and
dissemination. While the context of data quality assurance is one of the most
promising fields for applying machine learning, the lack of curated and labeled
training data is often a limiting factor.
The statistical production system for the Centralised Securities Database
features an interactive feedback loop between data collected by the European
Central Bank and data quality assurance performed by data quality managers at
National Central Banks. The quality assurance feedback loop is based on a set
of rule-based checks for raising exceptions, upon which the user either
confirms the data or corrects an actual error.
In this paper we use the information received from this feedback loop to
optimize the exceptions presented to the National Central Banks thereby
improving the quality of exceptions generated and the time consumed on the
system by the users authenticating those exceptions. For this approach we make
use of explainable supervised machine learning to (a) identify the types of
exceptions and (b) to prioritize which exceptions are more likely to require an
intervention or correction by the NCBs. Furthermore, we provide an explainable
AI taxonomy aiming to identify the different explainable AI needs that arose
during the project.

本文中介绍了一种基于机器学习和可解释人工智能的方法，用于通过优化规则检查中提出的例外，并提高相关数据质量，这种方法已应用在中央证券数据库的统计生产系统中。