12:35 - 13:20 Uhr
More and more of today's applications use AI systems developed through machine learning. The decisive factor here is not only that there is enough or even quite a lot of data available for learning, but also that this data is of sufficient quality. But what exactly is meant by quality? Many of the aspects of data quality that are known in the context of machine learning are rather qualitative in nature. How do such aspects become high quality? Can they also be made measurable? The talk will shed light on data quality issues and describe the challenges involved: How does one assess qualitative features as objectively as possible, what influence do these have on the quality of the model, and how can the explainability of AI models help to improve the data quality? For some of these questions, the talk will show approaches to solutions that were developed in the context of a scientific study. The presentation is aimed at software developers and testers, but also test managers who deal with artificial intelligent systems and for those who value data quality highly.
Gabriela Simion has been studying computer science at the Friedrich-Alexander University Erlangen-Nuremberg in a dual degree program with imbus AG in Möhrendorf since 2018. In her bachelor thesis, she is working on the topic of data quality for machine learning.
Dr. Gerhard Runze studied electrical engineering at the University of Erlangen-Nuremberg and received his doctorate. After many years of working as a developer, project and team leader in the telecommunications industry, he has been working at imbus AG in Möhrendorf as a test manager, trainer and consultant for embedded software and agile testing since 2015.