TY - CONF A1 - Meier, Sebastian A1 - Glinka, Katrin A2 - Stolze, Markus A2 - Loch, Frieder A2 - Baldauf, Matthias A2 - Alt, Florian A2 - Schneegass, Christina A2 - Kosch, Thomas A2 - Hirzle, Teresa A2 - Sadeghian, Shadan A2 - Draxler, Fiona A2 - Bektas, Kenan A2 - Lohan, Katrin A2 - Knierim, Pascal T1 - To Classify is to Interpret T2 - Proceedings of Mensch und Computer 2023 N2 - Taxonomy building is a task that requires interpreting and classifying data within a given frame of reference, which comes to play in many areas of application that deal with knowledge and information organization. In this paper, we explore how taxonomy building can be supported with systems that integrate machine learning (ML). However, relying only on black-boxed ML-based systems to automate taxonomy building would sideline the users’ expertise. We propose an approach that allows the user to iteratively take into account multiple model’s outputs as part of their sensemaking process. We implemented our approach in two real-world use cases. The work is positioned in the context of HCI research that investigates the design of ML-based systems with an emphasis on enabling human-AI collaboration. KW - Künstliche Intelligenz KW - Taxonomie KW - Maschinelles Lernen KW - Mensch-Maschine-Schnittstelle Y1 - 2023 UR - https://opus4.kobv.de/opus4-fhpotsdam/frontdoor/index/index/docId/3224 SN - 979-8-4007-0771-1 SP - 395 EP - 401 PB - Association for Computing Machinery CY - New York ER -