EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations
This work addresses the problem of optimizing interactive machine-learning systems for healthcare experts, though it is incremental as it builds on existing explanation methods.
The study investigated the effectiveness of global model-centric and data-centric explanations in helping healthcare experts detect and resolve data issues to improve prediction models, finding that a hybrid fusion of both explanation types was most effective for enhancing understanding and model improvement.
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.