Artificial intelligence (AI) has long been part of our everyday lives and continues to be a key technology of the future-more and more applications in business and everyday life are relying on it. To develop an AI application, a lot of data is needed: Training, test, and validation data. The quality of the used data is a growing concern: This data must not only be technically sound, but it must also ensure that the application operates in a non-discriminatory manner. It's also about the origin of the data, transparency, data protection, liability, and many other issues. In addition to the development of classical data cleaning methods, it is necessary to define the quality of data more generally and thus also consider ethical and legal boundaries. Until now, there have been hardly any uniform quality standards for this data, thus, the KITQAR research project aims to close this gap.

The KITQAR research project develops a “data quality framework” of standards to evaluate the quality of data used for AI applications. This includes quality requirements for AI test, validation, and training data. The project investigates these data in a scientific-technical consortium from informatics, ethical, and legal perspectives to cover the most diverse aspects of data quality and make them measurable and testable. To work in an application-oriented manner, the project draws on both data sets from practice and synthetic data.

• Prof. Dr. Felix Naumann
• Dr. Hazar Harmouch
• Sedir Mohammed