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

翻译：在交互式机器学习系统领域，解释机制在调试和优化预测模型过程中发挥着关键辅助作用。然而，全球模型导向与数据导向两类解释在多大程度上能有效帮助领域专家发现并解决潜在数据问题以改进模型，这一领域仍鲜有探索。本技术报告总结了我们两项用户研究的主要发现。研究系统性地考察了基于数据导向与模型导向的全局解释，在通过自动和手动数据配置优化机器学习模型的医疗专家支持系统中的影响。为实证探究这些动态机制，我们开展了两项用户研究：包含70位医疗专家的定量分析及30位医疗专家的定性评估。这些研究旨在阐明不同解释类型对信任度、可理解性及模型优化三个关键维度的影响。结果表明，仅依赖全局模型导向解释无法有效指导用户完成复杂的数据配置过程。相对而言，数据导向解释通过增强用户对配置后系统变更的理解展现了其潜力。但两者结合使用时，在促进医疗专家建立信任、提升可理解性及推动模型改进方面表现出最高效力。我们还提出了开发基于解释的交互式机器学习系统的重要启示，这些见解可指导创建更高效的系统，使领域专家能充分释放机器学习的潜力。