  Explainable AI (XAI) aims to provide insight into opaque model reasoning to
humans and as such is an interdisciplinary field by nature. In this paper, we
interviewed 10 practitioners to understand the possible usability of training
data attribution (TDA) explanations and to explore the design space of such an
approach. We confirmed that training data quality is often the most important
factor for high model performance in practice and model developers mainly rely
on their own experience to curate data. End-users expect explanations to
enhance their interaction with the model and do not necessarily prioritise but
are open to training data as a means of explanation. Within our participants,
we found that TDA explanations are not well-known and therefore not used. We
urge the community to focus on the utility of TDA techniques from the
human-machine collaboration perspective and broaden the TDA evaluation to
reflect common use cases in practice.
