As it becomes easier to collect large amounts of data it is more important than ever to make validated decisions over what data to use for automated decision making and sustain data sovereignty at the same time. To do so, the Fraunhofer ISST leveraged the International Data Spaces (IDS) reference architecture and developed a novel Data App, which ensures a high-quality standard in data transfers. To accomplish this, we realized a data quality analysis that determines the quality of a data set along several dimensions and established a connection with the Data Space Connector. This results in an easy-to-use tool for data quality analysis available to IDS users which leads to high-quality data sharing.

To validate and evaluate our results we collaborated with the Mondragon pilot on the integration of the data sovereignty concept in their AI pipeline. As part of this work, we wrote a collaborative paper on our experiences and lessons learned. This paper recently got accepted at the 1^st International Conference on AI Engineering (CAIN), which is co-located with the ICSE 2022 conference.