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.

翻译：在交互式机器学习系统中，解释功能有助于调试和改进预测模型。然而，全局模型中心和数据中心的多种解释在帮助领域专家检测并解决潜在数据问题以优化模型方面的有效性尚未得到充分探索。本研究探讨了在支持医疗专家通过自动化和手动数据配置优化模型的系统中，数据中心与模型中心全局解释的影响。我们针对医疗专家开展了定量研究（n=70）和定性研究（n=30），探究不同解释类型对可信度、可理解性及模型改进的影响。结果表明，全局模型中心解释在指导用户进行数据配置时存在不足。尽管数据中心解释增强了对配置后系统变化的理解，但两种解释类型的混合融合展现出最高的有效性。基于研究结果，我们还提出了面向高效解释驱动的交互式机器学习系统的设计启示。