QI2 -- an Interactive Tool for Data Quality Assurance

TL;DR

This paper presents QI2, an interactive tool designed to support comprehensive data quality assurance, ensuring compliance with legal standards and improving ML system safety through verification of data quality requirements.

Contribution

The paper introduces a novel approach and tool for verifying multiple data quality aspects, addressing legal and safety requirements for ML systems.

Findings

Demonstrated on MNIST dataset with handwritten digits

Supports verification of quantitative data quality requirements

Highlights benefits of interactive data quality assurance

Abstract

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.