Position: A Call to Action for a Human-Centered AutoML Paradigm
This is an incremental position paper calling for a new research direction in AutoML to improve usability for a broader audience.
The paper argues that AutoML has focused too much on predictive performance and needs to shift towards a human-centered paradigm that better integrates user interaction and expertise to meet its original goals of democratization and efficiency.
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.