With advanced AI/ML, there has been growing research on explainable AI (XAI) and studies on how humans interact with AI and XAI for effective human-AI collaborative decision-making. However, we still have a lack of understanding of how AI systems and XAI should be first presented to users without technical backgrounds. In this paper, we present the findings of semi-structured interviews with health professionals (n=12) and students (n=4) majoring in medicine and health to study how to improve onboarding with AI and XAI. For the interviews, we built upon human-AI interaction guidelines to create onboarding materials of an AI system for stroke rehabilitation assessment and AI explanations and introduce them to the participants. Our findings reveal that beyond presenting traditional performance metrics on AI, participants desired benchmark information, the practical benefits of AI, and interaction trials to better contextualize AI performance, and refine the objectives and performance of AI. Based on these findings, we highlight directions for improving onboarding with AI and XAI and human-AI collaborative decision-making.

翻译：随着人工智能/机器学习技术的进步，关于可解释人工智能的研究以及人类如何与AI及XAI进行有效协同决策的探讨日益增多。然而，我们仍缺乏对如何向非技术背景用户首次呈现AI系统及XAI的深入理解。本文通过半结构化访谈，收集了医疗健康领域专业人员（12人）及医学与健康专业学生（4人）的意见，研究如何利用AI与XAI改进入职培训流程。访谈基于人机交互准则，设计了针对脑卒中康复评估AI系统及其解释说明的入职培训材料，并向参与者进行了介绍。研究发现，除了传统的AI性能指标外，参与者更希望获得基准信息、AI的实际效益以及交互式试用机会，以便更好地理解AI性能的适用场景，并优化AI的目标设定与性能表现。基于这些发现，我们提出了改进AI与XAI入职培训及人机协同决策的未来发展方向。