000 Informatik, Wissen, Systeme
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- 2023 (2) (entfernen)
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- FB4 Design (2)
Developing the next generation of cartographers is essential to sustaining and expanding the mission of the International Cartographic Association. Students and early-career professionals today learn cartographic design alongside a diverse array of conceptual and technical competencies, such as data science, human-computer interaction, information visualization, and machine learning. Accordingly, the next generation of cartographers often are working at the forefront of the discipline, actively reshaping what cartography is and can be.
To Classify is to Interpret
(2023)
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.