Bridging Domain Expertise and AI through Data Understanding

With the ongoing digitalization of complex systems, for example in manufacturing, domain experts’ detailed understanding of datasets is pivotal to effectively training machine learning (ML) models. This understanding obtained through their deep domain knowledge, enables domain experts to collaborate with method experts to identify deficiencies in datasets, such as biases or anomalies, and curate them. Such curated datasets build the foundation for training effective ML models, which are able to inform subsequent decision-making processes. However, understanding the increasingly large and complex datasets and systems they represent is challenging. Therefore, this doctoral thesis investigates methods to support domain experts in building a solid data understanding for complex datasets. Specifically, the thesis focuses on three key areas: conceptualizing data understanding, augmenting domain knowledge through VIS4ML systems to curate datasets, and providing contextual information for AI-assisted decision-making. Initial findings indicate that VIS4ML systems effectively support domain experts in understanding and contextualizing datasets, enabling them to curate datasets collaboratively. This understanding, particularly when enriched through contextual information, shows promise in enhancing AI-assisted decision-making.