  During a research project in which we developed a machine learning (ML)
driven visualization system for non-ML experts, we reflected on
interpretability research in ML, computer-supported collaborative work and
human-computer interaction. We found that while there are manifold technical
approaches, these often focus on ML experts and are evaluated in
decontextualized empirical studies. We hypothesized that participatory design
research may support the understanding of stakeholders' situated sense-making
in our project, yet, found guidance regarding ML interpretability inexhaustive.
Building on philosophy of technology, we formulated explanation strategies as
an empirical-analytical lens explicating how technical explanations mediate the
contextual preferences concerning people's interpretations. In this paper, we
contribute a report of our proof-of-concept use of explanation strategies to
analyze a co-design workshop with non-ML experts, methodological implications
for participatory design research, design implications for explanations for
non-ML experts and suggest further investigation of technological mediation
theories in the ML interpretability space.
