  Explanations in interactive machine-learning systems facilitate debugging and
improving prediction models. However, the effectiveness of various global
model-centric and data-centric explanations in aiding domain experts to detect
and resolve potential data issues for model improvement remains unexplored.
This research investigates the influence of data-centric and model-centric
global explanations in systems that support healthcare experts in optimising
models through automated and manual data configurations. We conducted
quantitative (n=70) and qualitative (n=30) studies with healthcare experts to
explore the impact of different explanations on trust, understandability and
model improvement. Our results reveal the insufficiency of global model-centric
explanations for guiding users during data configuration. Although data-centric
explanations enhanced understanding of post-configuration system changes, a
hybrid fusion of both explanation types demonstrated the highest effectiveness.
Based on our study results, we also present design implications for effective
explanation-driven interactive machine-learning systems.
