  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.
