@inproceedings{MeierGlinka, author = {Meier, Sebastian and Glinka, Katrin}, title = {To Classify is to Interpret}, series = {Proceedings of Mensch und Computer 2023}, booktitle = {Proceedings of Mensch und Computer 2023}, editor = {Stolze, Markus and Loch, Frieder and Baldauf, Matthias and Alt, Florian and Schneegass, Christina and Kosch, Thomas and Hirzle, Teresa and Sadeghian, Shadan and Draxler, Fiona and Bektas, Kenan and Lohan, Katrin and Knierim, Pascal}, publisher = {Association for Computing Machinery}, address = {New York}, isbn = {979-8-4007-0771-1}, doi = {10.1145/3603555.3608532}, pages = {395 -- 401}, abstract = {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.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} }