In the realm of interactive machine-learning systems, the provision of explanations serves as a vital aid in the processes of debugging and enhancing prediction models. However, the extent to which various global model-centric and data-centric explanations can effectively assist domain experts in detecting and resolving potential data-related issues for the purpose of model improvement has remained largely unexplored. In this technical report, we summarise the key findings of our two user studies. Our research involved a comprehensive examination of the impact of global explanations rooted in both data-centric and model-centric perspectives within systems designed to support healthcare experts in optimising machine learning models through both automated and manual data configurations. To empirically investigate these dynamics, we conducted two user studies, comprising quantitative analysis involving a sample size of 70 healthcare experts and qualitative assessments involving 30 healthcare experts. These studies were aimed at illuminating the influence of different explanation types on three key dimensions: trust, understandability, and model improvement. Results show that global model-centric explanations alone are insufficient for effectively guiding users during the intricate process of data configuration. In contrast, data-centric explanations exhibited their potential by enhancing the understanding of system changes that occur post-configuration. However, a combination of both showed the highest level of efficacy for fostering trust, improving understandability, and facilitating model enhancement among healthcare experts. We also present essential implications for developing interactive machine-learning systems driven by explanations. These insights can guide the creation of more effective systems that empower domain experts to harness the full potential of machine learning

在互动式机器学习系统领域中，提供解释作为在调试和增强预测模型过程中的重要辅助。本技术报告总结了我们两个用户研究的主要发现，结果表明全局以模型为中心和以数据为中心的解释结合使用在指导用户进行数据配置复杂过程中展示出最高效果，对医疗专家的信任、理解性和模型改进具有积极影响。同时，文章还提出了开发互动式机器学习系统的关键启示，以激发领域专家充分发挥机器学习的潜力。

EXMOS用户研究取得的经验教训：一份技术报告，概述了对EXMOS平台进行用户研究以评估的主要收获