To Classify is to Interpret: Building Taxonomies from Heterogeneous Data through Human-AI Collaboration

Taxonomies serve this purpose as structured classification schemes that adhere to domain-specific standards. The importance of organizing, segmenting, and classifying data is especially obvious in light of the ever growing amount of information that is being created, aggregated, and made available through specialized data repositories or on the Internet. In light of the amount and heterogeneity of the available data, classification can hardly be addressed by means of manual-cognitive processing alone. Systems that integrate machine learning (ML) are able to process large amounts of data and, thus, can help with the task of classification and organization. However, delegating this task to ML-based systems in their entirety would mean that we sideline human interpretation and rely on the output of black-boxed systems that reproduce language ideologies and representational harms (see, e.g., [5]). As an attempt to highlight the interpretative character of classification and taxonomy building, we propose to design ML-based systems that enable human-AI collaboration. Such systems are designed with the goal to effectively combine human competencies and computational capabilities (see, e.g.,[27, 29]). Our approach enables domain experts to iteratively interact with the suggestions of the system while retaining interpretative authority. We report on the concept and implementation of this approach that we realized for two real-world use cases.