  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.
