Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) workflows, aiding the research of new ML algorithms, and contributing to the democratization of ML by making it accessible to a broader audience. Over the past decade, commendable achievements in AutoML have primarily focused on optimizing predictive performance. This focused progress, while substantial, raises questions about how well AutoML has met its broader, original goals. In this position paper, we argue that a key to unlocking AutoML's full potential lies in addressing the currently underexplored aspect of user interaction with AutoML systems, including their diverse roles, expectations, and expertise. We envision a more human-centered approach in future AutoML research, promoting the collaborative design of ML systems that tightly integrates the complementary strengths of human expertise and AutoML methodologies.

自动化机器学习（AutoML）的全面潜力在于解决目前未充分探索的用户与AutoML系统的交互方面，包括其不同角色、期望和专业知识，促进未来AutoML研究中更加以人为中心的方法，推动紧密整合人类专业知识和自动化机器学习方法的ML系统的协作设计。

人性化自动机器学习范式的行动呼吁