Position: A Call to Action for a Human-Centered AutoML Paradigm
<|reference_start|>Position: A Call to Action for a Human-Centered AutoML Paradigm: 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.<|reference_end|>
@article{lindauer2024position:,
    title={Position: A Call to Action for a Human-Centered AutoML Paradigm},
    author={Marius Lindauer and Florian Karl and Anne Klier and Julia Moosbauer
  and Alexander Tornede and Andreas Mueller and Frank Hutter and Matthias
  Feurer and Bernd Bischl},
    journal={arXiv preprint arXiv:2406.03348},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03348},
    primaryClass={cs.LG}
}