# QI2 -- an Interactive Tool for Data Quality Assurance

arXiv: https://arxiv.org/abs/2307.03419 · HTML: https://mlsanity.com/paper/2307.03419/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Data Quality Certification using ISO/IEC 25012: Industrial Experiences](https://mlsanity.com/paper/2102.11527/) (2021)

## Built on by (most cited first, 1 of 1)

- [ECS -- an Interactive Tool for Data Quality Assurance](https://mlsanity.com/paper/2307.04368/) (2023)

## Abstract (arXiv)

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.