Katrin Glinka

Sebastian Meier, Katrin Glinka

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.

Katrin Glinka, Patrick Tobias Fischer, Claudia Müller-Birn et al.

We present an exploratory case study describing the design and realisation of a ''pure mixed reality'' application in a museum setting, where we investigate the potential of using Microsoft's HoloLens for object-centred museum mediation. Our prototype supports non-expert visitors observing a sculpture by offering interpretation that is linked to visual properties of the museum object. The design and development of our research prototype is based on a two-stage visitor observation study and a formative study we conducted prior to the design of the application. We present a summary of our findings from these studies and explain how they have influenced our user-centred content creation and the interaction design of our prototype. We are specifically interested in investigating to what extent different constructs of initiative influence the learning and user experience. Thus, we detail three modes of activity that we realised in our prototype. Our case study is informed by research in the area of human-computer interaction, the humanities and museum practice. Accordingly, we discuss core concepts, such as gaze-based interaction, object-centred learning, presence, and modes of activity and guidance with a transdisciplinary perspective.

Claudia Müller-Birn, Katrin Glinka, Peter Sörries et al.

Researchers and practitioners increasingly consider a human-centered perspective in the design of machine learning-based applications, especially in the context of Explainable Artificial Intelligence (XAI). However, clear methodological guidance in this context is still missing because each new situation seems to require a new setup, which also creates different methodological challenges. Existing case study collections in XAI inspired us; therefore, we propose a similar collection of case studies for human-centered XAI that can provide methodological guidance or inspiration for others. We want to showcase our idea in this workshop by describing three case studies from our research. These case studies are selected to highlight how apparently small differences require a different set of methods and considerations. With this workshop contribution, we would like to engage in a discussion on how such a collection of case studies can provide a methodological guidance and critical reflection.